CN113781171A - Information pushing method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of artificial intelligence, and provides an information pushing method, an information pushing device, information pushing equipment and a storage medium, wherein the method comprises the following steps: receiving a trade order for a first item submitted by a user on a shopping application; determining a second item associated with the first item based on the attribute information of the first item; acquiring a recommended value of the second article; updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list; and displaying push information corresponding to the target recommendation list on a display page of the trading order. By the aid of the method and the device, accuracy of pushing can be improved.
Description
Technical Field
The application relates to the technical field of artificial intelligence, and mainly relates to an information pushing method, an information pushing device, information pushing equipment and a storage medium.
Background
Due to the rapid development of computer technology and electronic commerce, people are increasingly used to online shopping. At present, in order to enable a user to select an article which the user wants to buy more conveniently and efficiently, the online platform recommends the same or related article to the user through the search record and browsing history of the user. However, users sometimes search for an item, perhaps not for purchase, but for viewing the item's relevant parameters. If the item is pushed all the time, the pushing position of other items which may be purchased by the user is occupied, and the pushing accuracy is low. How to improve the accuracy of pushing is always a technical problem to be solved by those skilled in the art.
Disclosure of Invention
The embodiment of the application provides an information pushing method, an information pushing device, information pushing equipment and a storage medium, and the pushing accuracy can be improved.
In a first aspect, an embodiment of the present application provides an information pushing method, where:
receiving a trade order for a first item submitted by a user on a shopping application;
determining a second item associated with the first item based on the attribute information of the first item;
acquiring a recommended value of the second article;
updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list;
and displaying push information corresponding to the target recommendation list on a display page of the trading order.
In a second aspect, an embodiment of the present application provides an information pushing apparatus, where:
a communication unit for receiving a trade order for a first item submitted by a user on a shopping application;
the processing unit is used for determining a second item related to the first item based on the attribute information of the first item; acquiring a recommended value of the second article; updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list;
and the display unit is used for displaying the push information corresponding to the target recommendation list on a display page of the transaction order.
In a third aspect, an embodiment of the present application provides a computer device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for some or all of the steps described in the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, where the computer program makes a computer execute to implement part or all of the steps described in the first aspect.
The embodiment of the application has the following beneficial effects:
after the information pushing method, the information pushing device, the information pushing equipment and the storage medium are adopted, if a transaction order of a first article submitted by a user on a shopping application program is received, a second article related to the first article is determined based on attribute information of the first article. And then acquiring the recommended value of the second article, and updating the recommended list of the user based on the recommended value to obtain a target recommended list. And then, displaying push information corresponding to the target recommendation list on a display page of the trading order. Therefore, the push list can be updated based on the currently submitted transaction order, and the push accuracy can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
fig. 1 is a schematic flowchart of an information pushing method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an information pushing apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art without any inventive work according to the embodiments of the present application are within the scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The network architecture applied by the embodiment of the application comprises a server and electronic equipment. The number of the electronic devices and the number of the servers are not limited in the embodiment of the application, and the servers can provide services for the electronic devices at the same time. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like. The server may alternatively be implemented as a server cluster consisting of a plurality of servers.
The electronic device may be a Personal Computer (PC), a notebook computer, or a smart phone, and may also be an all-in-one machine, a palm computer, a tablet computer (pad), a smart television playing terminal, a vehicle-mounted terminal, or a portable device. The operating system of the PC-side electronic device, such as a kiosk or the like, may include, but is not limited to, operating systems such as Linux system, Unix system, Windows series system (e.g., Windows xp, Windows 7, etc.), Mac OS X system (operating system of apple computer), and the like. The operating system of the electronic device at the mobile end, such as a smart phone, may include, but is not limited to, an operating system such as an android system, an IOS (operating system of an apple mobile phone), a Window system, and the like.
The server is used for providing services for the electronic equipment. The electronic device in the embodiment of the application can install and run the application program, and the server can be a server corresponding to the application program installed in the electronic device and provide application service for the application program. The application program may be a single integrated application software, or an applet embedded in another application, or a system on a web page, etc., which is not limited herein.
For example, a user enters a search instruction for a target vocabulary on a page of a shopping application. And the electronic equipment receives the search instruction and sends a search request aiming at the target vocabulary to the server. The server responds to the search request and provides feedback search results to the electronic equipment. The electronic device displays the search result on a page of the shopping application, and the search result can comprise information such as image information and price information of a plurality of items corresponding to the target vocabulary. The information of the item in the search result may be displayed according to the relevance between the item and the target vocabulary, the sales volume and price of the item, and the like, which is not limited herein.
In the embodiment of the present application, the history of the user may be stored in advance. The history record may include a historical search record of the user in the shopping application, a historical browsing record of the user in the shopping application, a historical purchase record of the user, and the like. The history may alternatively include a history of what the user has, such as a record of searching, browsing, or purchasing items in other applications, etc. The history may alternatively include a user's history over a specified period of time. For example, the history of the past 618 activity period to the current time, or may be the past 618 activity period and the past twenty-one activity period, and the history of the present year search, etc. The history may alternatively be a history that is not deleted in the current shopping application, etc.
The history may include a search browsing record of the user from entering the shopping application to submitting a trade order for the first item. Several scenarios may be included, such as obtaining search results after a user enters a search term in a shopping application. A first item is selected from the search results and a trade order for the first item is submitted. As another example, after a user enters a page for a first item of a shopping application by copying text or scanning a two-dimensional code, the user submits a trade order for the first item. For another example, when the user enters the shopping application, a first item is selected from the currently displayed page or the pushed page, and a trade order for the first item is submitted, etc.
In the embodiment of the application, the recommendation list of the user can be stored in advance. Items determined based on the history may be included in the recommendation list. For example, browsing through items multiple times, repeatedly purchasing or associating items in a purchased order, unpouring items in wish information, etc. The purchased order may include, among other things, a trade order formed by the user purchasing an item in the shopping application. The purchased orders may include all purchase orders or may include purchase orders for the user over a specified period of time. The specified time period may be equal to or different from the specified time period of the historical search browsing records. The purchased order can be classified into a to-be-shipped item, a to-be-received item, an to-be-evaluated item, a refund item, an after-sale item, and the like according to the status. The purchased order may be obtained from a server corresponding to the shopping application.
The unpurchased wish information includes information on items that the user adds in the shopping cart of the shopping application. The unpurchased wish information may also include information on items that the user has marked as liked or that the user has collected, but not purchased. Such as a baby in a favorite, an item in a wish list, an item to be paid, etc. The unpurchased wishlist information can be obtained from shopping carts, favorites, wishlist, pending payment, and the like.
It is understood that the time length and/or the number of stay in each item searched or browsed by the user can be determined through the history of the user, so that whether the user is interested in the item can be determined according to the time length and/or the number of stay, the item which is interested by the user can be obtained, and the item which is not interested by the user can be obtained. And the history includes a search browsing record of the user from entering the shopping application to submitting a trade order for the first item. As such, by searching the browsing information, the user's psychological activities in browsing the item (or purchasing the first item), such as selection of related items, whether it is a hash, etc., can be guessed. Attribute information of items having a number of stays and/or a length of time greater than a threshold may also be analyzed to derive attributes of user preferences. In this manner, items that satisfy the attributes of the user preferences may be added to the recommendation list.
Items in the purchased order are purchased indicating that the user is interested in the items in the purchased order. Further, it may be determined whether the user is dissatisfied with the item based on whether the purchased order was returned. Or the user's interest in the item may be determined based on the number of repurchasings of the item in the purchased order. Or whether the user is satisfied with the item may be determined based on review information of the purchased order.
The items in the recommendation list may include items with an evaluation value greater than a threshold value in a purchased order, or repeatedly purchased items in a purchased order, or consumable items, etc. The evaluation value may be a value obtained by scoring according to evaluation information filled in by the user for each item, or evaluation information searched in the shopping application program or the network, or may be a value obtained by scoring based on sales volume information, time for adding unpulped wished orders, or similar values to items in purchased orders, and the like, and is not limited herein. It is understood that an evaluation value greater than a threshold value may indicate that the item has a high acceptance and is suitable for a repurchase. The repeatedly purchased articles can indicate that the user is satisfied with the use of the articles and are suitable for further repurchase. The consumable articles refer to articles with larger use demands, such as daily necessities like paper products, and are suitable for stock. The above items are pushed to be possible products to be purchased, and the pushing accuracy can be improved.
The items in the user's unpurchased information are items that the user wishes to purchase. The items in the recommendation list may include items that were browsed multiple times in the unpurchased wish information. It can be understood that the user browses the item many times, which means that the user has a strong purchasing intention for the item. Items that the user browses many times in the unpurchased wish information may be added to the recommendation list.
The items in the recommendation list may include items with shorter additions to the unpurchased wish information. It will be appreciated that if the time of joining the item in the unpurchased wish information is relatively long, this may indicate that the user has forgotten to purchase the item, or that the initial addition of the item may be for the purpose of completing a task, thereby determining that the item is not of interest to the user. Therefore, an item having a short addition time to the unpurcated wish information can be added to the recommendation list.
The history and recommended orders may also be stored in a block created on the blockchain network. The Blockchain (Blockchain) is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. The blockchain is essentially a decentralized database, which is a string of data blocks associated by using cryptography, each data block contains information of a batch of network transactions, and the information is used for verifying the validity (anti-counterfeiting) of the information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer. Therefore, data are stored in a distributed mode through the block chain, data security is guaranteed, and meanwhile data sharing of information among different platforms can be achieved.
The information pushing method provided by the embodiment of the application can be executed by an information pushing device, wherein the device can be realized by software and/or hardware, can be generally integrated in electronic equipment or a server, can update a pushing list based on a currently submitted trading order, and improves the pushing accuracy.
Referring to fig. 1, fig. 1 is a schematic flow chart of an information pushing method provided in the present application. Taking the application of the method to a server as an example for illustration, the method includes the following steps S101 to S105, wherein:
s101: a trade order for a first item submitted by a user on a shopping application is received.
The method and the system are not limited to the user, the shopping application program and the first article, and the trade order is used for achieving the trade of the first article. That is, after the user submits a trade order for the first item, a trade payment page for the first item may be generated, waiting for the user to complete the trade. After the user completes the transaction, a page may be displayed where the transaction is complete. It is noted that, after the user completes the trade order for the first item, the purchased order comprises the trade order for the first item completed by the user on the shopping application.
S102: determining a second item associated with the first item based on the attribute information of the first item.
In an embodiment of the application, the second item is associated with the first item. The attribute information of the first article may include a commodity type, which may be classified into a jacket, a trousers, etc. by parts; or may be classified by color as green, blue, red, etc.; or can be divided into leisure, formal, shoulder exposure, irregular, splicing and the like according to styles; or can be divided into men, women, children, old people, etc. according to the user types; or can be divided into liquid, solid and the like according to the shape of the article; or can be divided into daily use, electronic products, electronic cards and the like according to the attributes of the articles; or can be divided into 0-100, 100-500, 500-1000, 1000 or more according to the price of the article. The attribute information of the first article may include evaluation information and sales information of the first article, and the present application does not limit the type of the attribute information of the article.
The second item may be found according to the at least one attribute information described above. The second item may alternatively be determined based on a pre-stored mapping between each item and items associated with the same item type. The mapping may be a relationship between the items themselves, for example, a relationship between wine and wine sets, etc. The mapping may alternatively be based on analysis of data, which may be the purchase probability of the associated item in the associated order.
The associated order may be a trade order generated by a user in the shopping application (including the user in step S101 and other users) who purchases other items within a preset time period after purchasing the first item. Or may be other items selected for purchase in a recommendation page after the purchase of the first item, a trade order for the item, etc. Therefore, the second article related to the first article is searched through the mapping relation between each article and the related article, and the efficiency and the accuracy of determining the second article can be improved.
S103: and acquiring the recommended value of the second article.
In the embodiment of the application, the recommended value is used for describing the probability that the second item is pushed to the user and is bought by the user. The determination may be based on similarity values of the purchased item and the second item in the purchased order. It can be understood that if the second item is similar to the purchased item, and the purchased item is a consumable item or an item with a high repurchase probability, the purchase probability of the second item is high. If the second item is not similar to the purchased item, but the style of the second item is matched with the style of the user purchasing the item, the purchase probability of the second item is high.
The recommended value may be determined according to the similarity value between the article in the unpurchased wish information and the second article, and it can be understood that if the second article is similar to the article in the unpurchased wish information and indicates to be pushed again, the purchasing power of the user for purchasing the second article may be improved, and the purchasing probability of the second article is high. If the second item is not similar to the purchased item, the user is indicated to have a low purchase probability of purchasing such second item.
In one possible example, step S103 may include the following steps A1-A4, wherein:
a1: determining a recommendation probability for the second item based on the attribute information of the second item.
In the embodiment of the present application, the recommendation probability is used to describe the recommendation value of the product itself. It can be understood that, since the product in the purchased order is purchased, the probability of purchasing in a short time is small, and the recommendation probability is small. However, some products with high repurchase probability, such as skin products, clothes, toilet paper and the like, belong to products needing to be used, and the recommendation probability is high. The recommendation probability with better evaluation information is larger than the recommendation probability with worse evaluation information. The product which is in the unpurcated information for a long time has a recommendation probability of being purchased which is less than that of the newly added product. The recommendation probability for a product of the repeat type in the unpulped wish information and the purchased order is small. Therefore, the recommendation probability of the second article is determined based on the attribute information of the second article, the accuracy of determining the recommendation probability can be improved, and the accuracy of determining the recommendation value is improved.
In one possible example, step A1 includes the following steps A11-A13, wherein:
a11: and acquiring the recommendation score of the second item based on the attribute information of the second item.
In the embodiments of the present application, the recommendation score is used to describe the recommendation value of the second item itself. The recommendation score may be obtained by performing weighted calculation on evaluation values corresponding to the sales information and/or the evaluation information in the attribute information or the acquired sales information. Therefore, the accuracy rate of obtaining the recommendation score can be improved.
The sales information may include the amount of sales of the second item over a specified period of time (e.g., during the month or current activity), may also include a purchase profile of the second item, and so on. The sales volume refers to the number of the second item sold, and may include sales volume of a specific store (e.g., a flagship store) or a specific shopping application, or sales volume of the entire network of the second item. It will be appreciated that a purchase probability of a large sales amount may be greater than a purchase probability of a small sales amount. Therefore, the recommendation score of the second item is obtained based on the sales information, and the accuracy of determining the recommendation value is improved conveniently.
The purchase curve refers to a curve of sales and time. It will be appreciated that the purchase of different products may be made with different levels of effort in different seasons or periods of time. For example, the sales volume of cold drink products in summer may be greater than the sales volume of cold drink products in winter. Based on the above, the recommendation score of the second item is obtained based on the purchase curve of the second item, so that the accuracy of determining the recommendation value is further improved.
The content of the evaluation information can refer to the foregoing, and may be a numerical value obtained by scoring based on the evaluation information filled by the user for each product, or the evaluation information searched by the shopping application program or the network, or may be a numerical value obtained by scoring based on the sales volume information, the time of adding the unpulped wish order, or the similar value of the product in the bought order, and the like, which is not described herein again. It can be understood that the recommendation score of the second item is obtained based on the evaluation information of the second item, so that the accuracy of determining the recommendation value is improved.
A12: and acquiring the purchase probability of the second item after the first item is purchased based on the attribute information of the second item.
In the embodiment of the present application, the purchase probability may be obtained by counting the purchase data recorded in the shopping application after the first item is purchased. Wherein the purchase data may include a target trade order having a time interval less than a preset time period (e.g., half hour, 10 minutes, etc.) from a completion time of the trade order for the first item. In this way, the purchase probability can be obtained from a statistical point of view by taking the proportion of the second item in the target trade order as the purchase probability of the second item after the purchase of the first item.
The purchase probability may be obtained by an associated value between the second item and the first item, and the like, which is not limited herein. Wherein the association value may be determined for similarity between the first item and the second item, and for association of usage, etc. The associated value may be determined by determining a probability of purchasing the second item, or the like, as a difference between the price of the first item and the remaining usage amount of the user, the greater the difference, the greater the probability of purchasing the second item. In this way, the purchase probability is obtained from the perspective that the user will purchase the second item after purchasing the first item, and the accuracy of obtaining the purchase probability can be improved.
It should be noted that the execution order of step a11 and step a12 is not limited in the present application. That is, step A11 may be performed first, followed by step A12. Alternatively, step A12 may be performed first, followed by step A11. Or may be performed simultaneously.
A13: calculating a recommendation probability for the second item based on the purchase probability and the recommendation score.
In this embodiment, the recommendation probability may be a numerical value obtained by performing weighted calculation on the purchase probability and the recommendation score. Therefore, the accuracy of calculating the recommendation probability can be improved by setting the preset weight corresponding to the purchase probability and the recommendation score.
It is understood that in steps a 11-a 13, the recommendation score of the second item and the purchase probability of the second item after the purchase of the first item are obtained based on the attribute information of the second item. And then, the recommendation probability of the second article is calculated based on the recommendation score and the purchase probability, so that the accuracy of determining the recommendation probability can be improved.
A2: and selecting a target history record corresponding to the attribute information of the second article from the history records.
In this embodiment, the history of the user may be obtained from a cache in the electronic device, or may be obtained from a server corresponding to the shopping application, or obtained from the above block chain, and the like, which is not limited herein. It can be understood that the accuracy of obtaining the recommended value can be improved by obtaining the recommended value of the second item based on the history of the user.
The target history is the same history as or similar to the attribute information of the second item. Similar is understood to mean that the items are of the same type, similar in form, similar in use, etc. And acquiring the recommended value of the second article based on the target historical record, so that the accuracy of acquiring the recommended value can be further improved.
A3: determining a preference value of the user for the second item based on the target history.
In the present embodiment, the preference value is used to describe the probability that the user may like to purchase the second item. The recommended value of the item is determined based on the preference value of the item, and the accuracy of determining the recommended value can be improved. The method for determining the preference value is not limited in this application, and in one possible example, step A3 may include the following steps a31 and a32, wherein:
a31: and acquiring the average browsing duration and the purchasing success rate of the user based on the target historical record.
In the embodiment of the present application, the browsing duration may be a duration of browsing each item, and may include a purchase duration from searching to purchasing the item. The average browsing duration may be an average of browsing durations of browsing items in the respective target search browsing records, or may be an average of purchase durations spent purchasing items in the respective target search browsing records, and the like.
The purchase success rate is used to describe the probability of purchasing an item in the target search browsing record, and may be determined based on the time of the purchased order and the time of the target search browsing record, or based on the association between the search term and the last purchased item.
A32: calculating a preference value of the second item based on the average browsing duration and the purchase success rate.
In the embodiment of the application, the evaluated value can be determined based on a numerical value obtained by performing weighted calculation on the average browsing duration and the purchase success rate; or may be calculated based on a product between the first preference value corresponding to the average browsing duration and the purchase success rate, and the like, which is not limited herein.
It is understood that, in the steps a31 and a32, the accuracy of determining the preference value can be improved by calculating the preference value of the second item based on the average browsing duration and the purchase success rate of the user acquired from the target history.
A4: and determining the recommended value of the second article according to the recommended probability and the preference value.
In this embodiment of the application, the recommendation value may be a product between the preference value and the recommendation probability, or may be a numerical value obtained by performing weighted calculation on the preference value and the recommendation probability, and the like, which is not limited herein.
It can be understood that, in steps a 1-a 4, the recommendation probability and the preference value of the second item are determined first, and then the recommendation value of the second item is determined based on the recommendation probability and the preference value, so that the accuracy of determining the preference value can be improved.
S104: and updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list.
In this embodiment of the application, the recommendation list of the user may be obtained from a cache in the electronic device, or may be obtained from a server corresponding to the shopping application, or obtained from the above block chain, and the like, which is not limited herein. It can be understood that the push accuracy can be improved by updating the recommendation list of the user.
The target recommendation list is the recommendation list after being updated, the method for updating the recommendation list is not limited in the application, and the pushing sequence of the items related to the first item in the recommendation list can be advanced. It will be appreciated that a user may purchase the associated item after purchasing the first item. For example, buying wine, perhaps wine sets; when a mobile phone is bought, a mobile phone shell, a toughened film and an earphone can be bought; air conditioners are bought, and washing machines, televisions and the like are also probably bought.
In one possible example, step S104 includes the steps of: if the third article in the user recommendation list is successfully matched with the second article, replacing the recommendation value of the third article with the recommendation value of the second article to obtain a reference recommendation list; selecting a fourth article with the recommended value larger than a first threshold value from the second articles, and selecting a fifth article with the recommended value smaller than a second threshold value; if the fourth item does not belong to the reference recommendation list, adding the fourth item to the reference recommendation list; and if the fifth item belongs to the reference recommendation list, deleting the fifth item in the reference recommendation list.
In this embodiment, the third item is an item successfully matched with the second item in the recommendation list of the user. That is, the matching value between the attribute information of the third item and the attribute information of the second item is 1 or greater than a specified threshold (e.g., 80%, 90%, etc.). It is understood that if the third item and the second item are successfully matched, the recommended value before the transaction order including the first item is submitted is included in the recommendation list. Therefore, after the transaction order is submitted, the recommendation values of the items in the recommendation list are updated, and the pushing accuracy and the pushing real-time performance are improved.
The reference recommendation list is a recommendation list in which the recommendation value of the third item is replaced with the recommendation value of the second item. And if the third article successfully matched with the second article is not included in the recommendation list, the reference recommendation list is equal to the recommendation list.
The recommended value of the fourth item is greater than the first threshold value, and the recommended value of the fifth item is less than the second threshold value. The first threshold and the second threshold are not limited in the present application, and the first threshold is greater than or equal to the second threshold. The first threshold and the second threshold may be determined based on an average of recommended values of items in the recommendation list, and the like.
It is understood that the recommended value of the fourth item is greater than the first threshold, which indicates that the fourth item is worth pushing. If the fourth item belongs to the reference recommendation list updated by the user, the fourth item does not need to be added, otherwise, the fourth item needs to be added to the reference recommendation list. And if the recommended value of the fifth item is less than the second threshold value, the fifth item is not worth pushing. If the fifth item belongs to the reference recommendation list of the user, the fifth item may be deleted from the reference recommendation list, otherwise, the fifth item does not need to be deleted.
In this example, it is first determined whether the user's recommendation list includes a third item that successfully matches the second item. If yes, updating the recommendation list to obtain a reference recommendation list. Therefore, the pushing accuracy and the pushing real-time performance can be improved. And acquiring a fourth item and a fifth item from the second item based on the recommended value of the second item and the size relationship between the first threshold and the second threshold. And updating the reference recommendation list based on whether the fourth item and the fifth item are in the reference recommendation list of the user. Therefore, the fifth product which is not recommended in the recommendation list can be deleted, and the fourth product which is worth recommending is added, so that the accuracy rate and the real-time performance of recommendation are further improved.
In one possible example, the recommendation list includes a sixth item in the unpurchased wish information, the method further comprising: acquiring the adding time of the sixth article and/or the similarity value between the sixth article and the first article; and if the similarity value is greater than a third threshold value or the joining time is greater than a fourth threshold value, deleting and determining the sixth item from the recommendation list.
The sixth item is an item related to the unpurchased wish information. The similarity value between the sixth item and the first item may be determined based on a degree of similarity between the attribute information of the sixth item and the attribute information of the first item.
The third threshold and the fourth threshold are not limited in the present application. The third threshold may be determined based on the number of second items associated with the first item, and so on. The fourth threshold may be determined based on an average of joining times of all sixth items in the unpurchased wish information, and the like.
It is to be understood that if the similarity value between the sixth item and the first item is greater than the third threshold, it indicates that the sixth item and the first item are of the same type, the probability of repeated purchase is small, and the sixth item may be deleted from the recommendation list. If the adding time of the sixth item into the unpulped wish information is greater than the fourth threshold, the purchasing intention of the user on the sixth item is not strong, the sixth item can be deleted from the recommendation list, and the pushing accuracy is improved.
S105: and displaying push information corresponding to the target recommendation list on a display page of the trading order.
In this embodiment of the application, the display page of the trade order may be a page for generating a trade payment after the trade order is submitted, or may be a page after the trade is completed, and the like, which is not limited herein. The method for displaying the push information corresponding to the target recommendation list is not limited in the present application, and in a possible example, the step S105 includes the following steps B1 to B4, where:
b1: and selecting a target item from the items in the target recommendation list.
In the embodiment of the application, the target item is an item selected from the target recommendation list and pushed to the user. The method for selecting the target articles is not limited, and the target articles can be sorted based on the recommended values of the articles, and the target articles in the preset number are selected. The recommended value of the item associated with the first item may refer to the description of step a2, and will not be described herein. The recommendation value for the item not associated with the first item may be obtained with reference to the previously tagged recommendation value in the recommendation list. The originally marked recommendation value may be obtained based on the recommendation probability obtained by the attribute information of the article, and the like, and is not limited herein. The number of the active elements is not limited in the present application, and may be one or more, and usually is multiple.
B2: and extracting keywords of the target item based on the attribute information of the target item.
In the embodiment of the present application, the keyword may be an advertisement, activity strength, and other object characteristics of the target object. In one possible example, step B2 may include the steps of: classifying the attribute information of the target object to obtain at least two sub-attribute information groups; determining an item characteristic of the sub-attribute information group; determining keywords of the target item based on the item features.
The sub-attribute information group may be classified based on attribute information of the article, such as color, shape, production date, sales volume, advertising language, speaker, comment information, and the like. The keyword may be a combination of the article features of all the sub-article information groups, or may be a combination of selected article features among the article features of all the sub-article information groups.
It is to be appreciated that in this example, the attribute information of the target item is first classified. And determining the article characteristics of each group of the sub-attribute information groups obtained by classification, and determining the keywords of the target article based on the article characteristics. Thus, the representativeness and the accuracy of the determined keywords can be improved.
B3: and generating push information of the target item based on the keyword and the recommended value of the target item.
In this embodiment of the application, the push information of the target item may be information corresponding to the keyword and the recommended value. Or the target item may be a keyword selected based on the recommendation value, for example, if the number of the target items is 3, then 3 keywords may be selected as the push information of the target item corresponding to the maximum recommendation value. And selecting 2 keywords as the push information of the target object corresponding to the intermediate recommended value. The target item corresponding to the minimum recommended value may select 1 keyword as the push information of the target item.
The push information of the target item may include an item characteristic, a price or activity strength of the target item, and may further include a recommended value of the target item. If the number of the target items is greater than or equal to 2, the push information may include push information of each target item, or may include a common characteristic in the push information of the target items.
B4: and displaying the push information.
In the embodiment of the application, the push information of the target object can be sequentially pushed to the user according to the recommended value, so that the push information is displayed in the electronic equipment of the user. Or the keywords of the target item can be sequentially pushed to the user according to the recommended value, and then the pushed content of the target item corresponding to the keywords selected by the user can be pushed.
It can be understood that, in steps B1-B4, the push accuracy can be improved by selecting the target item from the items in the target recommendation list. And then, generating push information of the target object based on the recommended value of the target object and the keywords extracted from the attribute information of the target object, and then pushing the push information to the user, so that the push effect can be improved.
In the method shown in FIG. 1, if a trade order for a first item submitted by a user on a shopping application is received, a second item associated with the first item is determined based on attribute information of the first item. And then acquiring a recommended value of the second article, and updating the recommended list of the user based on the recommended value to obtain a target recommended list. And then, displaying push information corresponding to the target recommendation list on a display page of the trading order. Therefore, the push list can be updated based on the currently submitted transaction order, and the push accuracy can be improved.
The method of the embodiments of the present application is set forth above in detail and the apparatus of the embodiments of the present application is provided below.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an information pushing apparatus according to the present application, and as shown in fig. 2, the information pushing apparatus 200 includes:
a communication unit 201 for receiving a trade order for a first item submitted by a user on a shopping application;
a processing unit 202, configured to determine a second item associated with the first item based on the attribute information of the first item; acquiring a recommended value of the second article; updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list;
a display unit 203, configured to display, on a display page of the trade order, push information corresponding to the target recommendation list.
In one possible example, the processing unit 202 is specifically configured to determine a recommendation probability of the second item based on the attribute information of the second item; selecting a target history record corresponding to the attribute information of the second article from the history records; determining a preference value of the user for the second item based on the target history; and calculating the recommended value of the second article according to the recommended probability and the preference value.
In a possible example, the processing unit 202 is specifically configured to obtain a recommendation score of the second item based on the attribute information of the second item; acquiring the purchase probability of the second item after the first item is purchased based on the attribute information of the second item; calculating a recommendation probability for the second item based on the purchase probability and the recommendation score.
In a possible example, the processing unit 202 is specifically configured to obtain an average browsing duration and a purchase success rate of the user based on the target history; calculating a preference value of the second item based on the average browsing duration and the purchase success rate.
In a possible example, the processing unit 202 is specifically configured to, if a third item in the recommendation list of the user is successfully matched with the second item, replace the recommendation value of the third item with the recommendation value of the second item to obtain a reference recommendation list; selecting a fourth article with the recommended value larger than a first threshold value from the second articles; selecting a fifth article of which the recommended value is smaller than a second threshold value from the third articles; if the fourth item does not belong to the reference recommendation list, adding the fourth item to the reference recommendation list; or if the fifth item belongs to the reference recommendation list, deleting the fifth item in the reference recommendation list.
In a possible example, the recommendation list includes a sixth item in the unpurchased wish information, and the processing unit 202 is further configured to obtain a joining time and/or a similarity value between the sixth item and the first item; and if the similarity value is greater than a third threshold value or the joining time is greater than a fourth threshold value, deleting and determining the sixth item from the recommendation list.
In one possible example, the processing unit 202 is further configured to select a target item from the items in the target recommendation list; extracting keywords of the target item based on the attribute information of the target item; generating push information of the target item based on the keyword and the recommended value of the target item; the display unit 203 is specifically configured to display the push information.
For detailed processes executed by each unit in the information pushing apparatus 200, reference may be made to the execution steps in the foregoing method embodiments, which are not described herein again.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure. As shown in fig. 3, the computer device 300 includes a processor 310, a memory 320, a communication interface 330, and one or more programs 340. The processor 310, the memory 320, and the communication interface 330 are interconnected via a bus 350. The relevant functions implemented by the processing unit 202 and the display unit 203 shown in fig. 2 may be implemented by the processor 310. The related functions implemented by the display unit 203 shown in fig. 2 can also be implemented by a display not shown in fig. 3. The related functions implemented by the communication unit 201 shown in fig. 2 can be implemented by the communication interface 330.
The one or more programs 340 are stored in the memory 320 and configured to be executed by the processor 310, the programs 340 including instructions for:
receiving a trade order for a first item submitted by a user on a shopping application;
determining a second item associated with the first item based on the attribute information of the first item;
acquiring a recommended value of the second article;
updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list;
and displaying push information corresponding to the target recommendation list on a display page of the trading order.
In one possible example, in terms of obtaining the recommended value of the second item, the program 340 is specifically configured to execute the following steps:
determining a recommendation probability of the second item based on the attribute information of the second item;
selecting a target history record corresponding to the attribute information of the second article from the history records;
determining a preference value of the user for the second item based on the target history;
and calculating the recommended value of the second article according to the recommended probability and the preference value.
In one possible example, in the determining the recommendation probability for the second item based on the attribute information of the second item, the program 340 is specifically configured to execute the following steps:
acquiring a recommendation score of the second item based on the attribute information of the second item;
acquiring the purchase probability of the second item after the first item is purchased based on the attribute information of the second item;
calculating a recommendation probability for the second item based on the purchase probability and the recommendation score.
In one possible example, in the determining the user's preference value for the second item based on the target history, the program 340 is specifically configured to execute the instructions of:
acquiring the average browsing duration and the purchasing success rate of the user based on the target historical record;
calculating a preference value of the second item based on the average browsing duration and the purchase success rate.
In one possible example, in terms of updating the recommendation list of the user based on the recommendation value of the second item to obtain a target recommendation list, the program 340 is specifically configured to execute the following steps:
if the third article in the user recommendation list is successfully matched with the second article, replacing the recommendation value of the third article with the recommendation value of the second article to obtain a reference recommendation list;
selecting a fourth article with the recommended value larger than a first threshold value from the second articles;
selecting a fifth article of which the recommended value is smaller than a second threshold value from the third articles;
if the fourth item does not belong to the reference recommendation list, adding the fourth item to the reference recommendation list; or
And if the fifth item belongs to the reference recommendation list, deleting the fifth item in the reference recommendation list.
In one possible example, where the recommendation list includes a sixth item in the unpulped wish information, the program 340 is further for instructions to:
acquiring the adding time of the sixth article and/or the similarity value between the sixth article and the first article;
and if the similarity value is greater than a third threshold value or the joining time is greater than a fourth threshold value, deleting and determining the sixth item from the recommendation list.
In one possible example, in terms of displaying the push information corresponding to the target recommendation list, the program 340 is specifically configured to execute the following steps:
selecting a target item from the items in the target recommendation list;
extracting keywords of the target item based on the attribute information of the target item;
generating push information of the target item based on the keyword and the recommended value of the target item;
and displaying the push information.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for causing a computer to execute to implement part or all of the steps of any one of the methods described in the method embodiments, and the computer includes an electronic device and a server.
Embodiments of the application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform to implement some or all of the steps of any of the methods recited in the method embodiments. The computer program product may be a software installation package, the computer comprising an electronic device and a server.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art will also appreciate that the embodiments described in this specification are presently preferred and that no particular act or mode of operation is required in the present application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, at least one unit or component may be combined or integrated with another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may also be distributed on at least one network unit. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a hardware mode or a software program mode.
The integrated unit, if implemented in the form of a software program module and sold or used as a stand-alone product, may be stored in a computer readable memory. With such an understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer (which may be a personal computer, a server, a network device, or the like) to execute all or part of the steps of the methods according to the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An information pushing method, comprising:
receiving a trade order for a first item submitted by a user on a shopping application;
determining a second item associated with the first item based on the attribute information of the first item;
acquiring a recommended value of the second article;
updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list;
and displaying push information corresponding to the target recommendation list on a display page of the trading order.
2. The method of claim 1, wherein obtaining the recommended value for the second item comprises:
determining a recommendation probability of the second item based on the attribute information of the second item;
selecting a target history record corresponding to the attribute information of the second article from the history records;
determining a preference value of the user for the second item based on the target history;
and calculating the recommended value of the second article according to the recommended probability and the preference value.
3. The method of claim 2, wherein determining the recommendation probability for the second item based on the attribute information of the second item comprises:
acquiring a recommendation score of the second item based on the attribute information of the second item;
acquiring the purchase probability of the second item after the first item is purchased based on the attribute information of the second item;
calculating a recommendation probability for the second item based on the purchase probability and the recommendation score.
4. The method of claim 2, wherein determining the user's preference value for the second item based on the target history comprises:
acquiring the average browsing duration and the purchasing success rate of the user based on the target historical record;
calculating a preference value of the second item based on the average browsing duration and the purchase success rate.
5. The method according to any one of claims 1-4, wherein the updating the recommendation list of the user based on the recommendation value of the second item to obtain a target recommendation list comprises:
if the third article in the user recommendation list is successfully matched with the second article, replacing the recommendation value of the third article with the recommendation value of the second article to obtain a reference recommendation list;
selecting a fourth article with the recommended value larger than a first threshold value from the second articles, and selecting a fifth article with the recommended value smaller than a second threshold value;
if the fourth item does not belong to the reference recommendation list, adding the fourth item to the reference recommendation list; or
And if the fifth item belongs to the reference recommendation list, deleting the fifth item in the reference recommendation list.
6. The method of claim 5, wherein the third item comprises a sixth item in the unpopulated wish information, the method further comprising:
acquiring the adding time of the sixth article and/or the similarity value between the sixth article and the first article;
and if the similarity value is greater than a third threshold value or the joining time is greater than a fourth threshold value, deleting and determining the sixth item from the recommendation list.
7. The method according to any one of claims 1-4, wherein the displaying the push information corresponding to the target recommendation list comprises:
selecting a target item from the items in the target recommendation list;
extracting keywords of the target item based on the attribute information of the target item;
generating push information of the target item based on the keyword and the recommended value of the target item;
and displaying the push information.
8. An information pushing apparatus, comprising:
a communication unit for receiving a trade order for a first item submitted by a user on a shopping application;
the processing unit is used for determining a second item related to the first item based on the attribute information of the first item; acquiring a recommended value of the second article; updating the recommendation list of the user based on the recommendation value of the second article to obtain a target recommendation list;
and the display unit is used for displaying the push information corresponding to the target recommendation list on a display page of the transaction order.
9. A computer device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps of the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program causing a computer to execute to implement the method of any one of claims 1-7.
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