CN110175883A - Sorting method, sorting device, electronic equipment and nonvolatile storage medium - Google Patents
Sorting method, sorting device, electronic equipment and nonvolatile storage medium Download PDFInfo
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Abstract
The embodiment of the invention relates to the field of artificial intelligence, and discloses a sorting method, sorting equipment and a storage medium. In the invention, the sorting method comprises the following steps: determining a first characteristic factor according to the information of the commodity, wherein the commodity corresponds to the activity scene; determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of the user and association information between the merchant and the user; determining the score value of the commodity according to the first characteristic factor and the second characteristic factor; and sorting the commodities according to the score values of the commodities. Aiming at different activity scenes, the first characteristic factors are flexibly configured and combined with the second characteristic factors, the intelligent commodity sequencing models with different activity themes can be rapidly built, and then commodities are sequenced, so that the commodity display of thousands of people and thousands of faces is realized, accurate recommendation is provided for users, the user requirements are more met, and the user experience is further improved.
Description
Technical Field
The embodiment of the invention relates to the field of artificial intelligence, in particular to a sorting method, a sorting device, electronic equipment and a nonvolatile storage medium.
Background
At present, when buying take-out, a user makes an order transaction with a merchant through a take-out platform. In order to find out the potential ordering will of the user, the takeout platform intelligently recommends the user based on the dimension of the dishes, for example, intelligently sorts the dishes according to different topics such as popularity ranking, goodness, price preference and the like, and displays the sorting result to the user.
The inventors found that at least the following problems exist in the related art: when intelligent recommendation is performed on a user, the user can hope to obtain recommendation sequencing of different dimensions, and various recommendation subjects exist in the different dimensions. The method comprises the steps that log data of recommended topics based on dish dimensions are few, the internal association relation between dishes and corresponding recommended topics cannot be mined from the few data, and a user cannot obtain a desired dish recommended list; moreover, because the number of users and the number of dishes of the takeout platform are in the hundred million level, and interactive data between the users and the dishes is small, the text data of the dishes is generally formed by using information such as names of the dishes or dish evaluations, and if the text data is converted into numerical representation in a mathematical model, the calculated amount is very large, a good sequencing result cannot be obtained, and then the users cannot obtain a desired dish recommendation list, and the user experience is reduced.
Disclosure of Invention
The embodiment of the invention aims to provide a sorting method, a sorting device and a storage medium, which are used for solving the problem that a sales platform cannot accurately recommend dishes which adapt to different dimensions.
In order to solve the above technical problem, an embodiment of the present invention provides a sorting method, including the following steps: determining a first characteristic factor according to the information of the commodity, wherein the commodity corresponds to the activity scene; determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of the user and association information between the merchant and the user; determining the score value of the commodity according to the first characteristic factor and the second characteristic factor; and sorting the commodities according to the score values of the commodities.
The embodiment of the invention also provides a sequencing device, which comprises: the first characteristic module is used for determining a first characteristic factor according to the information of the commodity, wherein the commodity corresponds to the activity scene; the second characteristic module is used for determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of the user and correlation information between the merchant and the user; the commodity score module is used for determining the score value of the commodity according to the first characteristic factor and the second characteristic factor; and the sequencing module is used for sequencing the commodities according to the score values of the commodities.
Embodiments of the present invention further provide an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor executes the program to perform: determining a first characteristic factor according to the information of the commodity, wherein the commodity corresponds to the activity scene; determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of the user and association information between the merchant and the user; determining the score value of the commodity according to the first characteristic factor and the second characteristic factor; and sorting the commodities according to the score values of the commodities.
Embodiments of the present invention also provide a non-volatile storage medium for storing a computer-readable program for a computer to execute the above sorting method.
Compared with the prior art, the implementation mode of the invention has the main differences and the effects that: according to different activity scenes, the first characteristic factors are flexibly configured, the second characteristic factors are combined, the score values of commodities are determined, the intelligent commodity sorting models with different activity themes can be rapidly built, then the commodities are sorted through the score values, the commodity display of thousands of people is realized, accurate recommendation is provided for users, the commodity sorting model is closer to user requirements, and the user experience is further improved.
In addition, determining a score value of the commodity according to the first characteristic factor and the second characteristic factor comprises the following steps: sorting the commodities according to the first characteristic factor, and determining a first characteristic number of the commodities; sorting the commercial tenants to which the commodities belong according to the second characteristic factors, and determining second characteristic numbers of the commercial tenants; and performing weighted fusion on the first characteristic number and the second characteristic number to obtain the score value of the commodity.
In the method, the score value of the commodity is obtained by weighting and fusing the first characteristic number and the second characteristic number, so that the score value can better reflect the influence of the first characteristic factor, or can better reflect the influence of the second characteristic factor, and the score value obtained by the first characteristic number and the second characteristic number can better reflect the characteristics of the corresponding characteristic factor, so that a merchant can pertinently improve the corresponding commodity through the score value to adapt to the requirements of different activity scenes, and the experience effect of a user on a sales platform is improved while better profits are brought to the sales platform.
In addition, the method for obtaining the score value of the commodity by weighting and fusing the first feature number and the second feature number comprises the following steps: determining a weight corresponding to the first feature number and a weight corresponding to the second feature number; calculating the product of the weight corresponding to the first feature number and the first feature number to obtain a first product value; calculating the product of the weight corresponding to the second feature number and the second feature number to obtain a second product value; the sum of the first product value and the second product value is calculated, and the resultant sum value is used as the score value of the commodity.
In addition, determining the weight corresponding to the first feature number and the weight corresponding to the second feature number includes: determining weights of the first characteristic factor and the second characteristic factor; and determining a weight corresponding to the first feature number and a weight corresponding to the second feature number according to the weights.
In the method, the weight of the first characteristic factor and the second characteristic factor reflects which characteristic factor is more emphasized in the process of calculating the score value of the commodity, and if the weight of the first characteristic factor is emphasized, the factor which indicates that the score value of the commodity more refers to the linkage factor of the commodity is represented; if the weight is different from the second characteristic factor, the weight corresponding to the first characteristic number and the weight corresponding to the second characteristic number are determined, so that the requirement of a user can be reflected by the commodity sequencing, the commodity can flexibly adapt to the requirements of different activity scenes, the business of a merchant is improved, and better benefits are obtained.
In addition, the association information between the merchant and the user includes: interactive information of the user and the merchant and context information when the user accesses the merchant; determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of the user and association information between the merchant and the user, wherein the determining of the second characteristic factor comprises the following steps: inputting at least one of information of a merchant to which the commodity belongs, interactive information when the user accesses the merchant, information of the user and context information when the user accesses the merchant into the data model, and determining a second characteristic factor of the merchant to which the commodity belongs.
In the mode, the data model is used, so that the second characteristic factor with general adaptability can be obtained, the second characteristic factor can reflect the information of the commercial tenant better, the second characteristic factor can comprehensively evaluate the corresponding commodity, and the commodity can adapt to the requirements of different activity meeting places by pertinently improving the commodity.
In addition, the step of sequencing the commodities according to the first characteristic factor and determining the first characteristic number of the commodity comprises the following steps: obtaining the number of recalled commodities; and determining a first characteristic number of the commodity according to the first characteristic factor and the number of the recalled commodities.
In addition, the information of the merchant to which the commodity belongs includes: the flow statistical information of the commercial tenant and the attribute information of the commercial tenant.
In addition, the information of the user includes: traffic statistics information of the user and attribute information of the user.
In addition, the interactive information when the user visits the merchant includes: interactive traffic statistics and pricing information; context information when a user accesses a merchant, including: access information, delivery information, and network information.
In addition, the information of the commodity includes: at least one item of monthly sales of the commodities, good appraisal rate of the commodities, delivery time corresponding to the commodities and price information corresponding to the commodities.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
FIG. 1 is a flow chart of a sorting method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a sorting method according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a sorting apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments.
The terms "first," "second," and the like in the description and in the claims, and in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be implemented in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
A first embodiment of the present invention relates to a sorting method. Aiming at different activity scenes, the first characteristic factors are flexibly configured and combined with the second characteristic factors, the intelligent commodity sequencing models with different activity themes can be rapidly built, and then commodities are sequenced, so that the commodity display of thousands of people and thousands of faces is realized, accurate recommendation is provided for users, the user requirements are more met, and the user experience is further improved.
The following describes the implementation details of the sorting method in the present embodiment in detail, and the following is only for facilitating understanding of the implementation details of the present embodiment and is not necessary for implementing the present embodiment.
Fig. 1 is a flowchart illustrating a sorting method in this embodiment, which is applied to a server and may include the following steps.
In step 101, a first characteristic factor is determined based on information of the commodity.
Wherein the goods correspond to the activity scene. For example, when a user selects a commodity and wants to select a commodity with a preferential price, the activity scene is a price preferential scene, and in the price preferential scene, the server screens out the corresponding commodity according to price information corresponding to the commodity and presents the commodity to the user; if the user wants to obtain the commodity with higher evaluation degree, the activity scene is a favorable evaluation scene, and in the favorable evaluation scene, the server screens out the corresponding commodity according to the favorable evaluation rate of the commodity and presents the commodity to the user.
Wherein, the information of the commodity includes: at least one item of monthly sales of the commodities, good appraisal rate of the commodities, delivery time corresponding to the commodities and price information corresponding to the commodities.
It should be noted that the first characteristic factor includes one or more linkage factors of the commodity, the linkage factor is selected according to the mind of the user and the product form, each linkage factor corresponds to information of a commodity, for example, monthly sales of the commodity is used as a first linkage factor, the good rating of the commodity is used as a second linkage factor, the delivery time corresponding to the commodity is used as a third linkage factor, the price information corresponding to the commodity is used as a fourth linkage factor, and then the first characteristic factor is composed of the first linkage factor and the second linkage factor, or the first characteristic factor is composed of the first linkage factor to the fourth linkage factor. The reason why the above four kinds of linkage factors are used as the first feature factor is that the linkage factors can reflect the overall evaluation of the product by the user, for example, the monthly sales volume of the product is used as the first feature factor, and if the monthly sales volume of the product is higher, the user is shown to like the product more.
In step 102, a second characteristic factor is determined according to at least one of information of a merchant to which the commodity belongs, information of the user, and association information between the merchant and the user.
Wherein, the associated information between the merchant and the user comprises: interaction information when the user accesses the merchant and context information when the user accesses the merchant.
It should be noted that, at least one of the information of the merchant to which the commodity belongs, the information of the user, the interaction information when the user accesses the merchant, and the context information when the user accesses the merchant is used to determine a second characteristic factor, where the second characteristic factor reflects the information of the user accessing the merchant and what kind of user is willing to access the merchant, and the total merchant information score obtained based on the above information can reflect the total evaluation of the user on the merchant according to the merchant information score.
The information of the merchant to which the commodity belongs comprises: the flow statistical information of the commercial tenant and the attribute information of the commercial tenant.
It should be noted that the traffic statistic information of the merchant specifically includes: the clicked times, the issued times, the exposed times, the clicked rate, the visited rate and the like of the commodities of the merchant in a period of time; the attribute information of the merchant specifically includes: whether the merchant has full or full activities, whether the merchant is a new shop, a payment mode, address information of the physical shop of the merchant and the like.
The information of the user comprises: traffic statistics information of the user and attribute information of the user.
It should be noted that the traffic statistic information of the user specifically includes: the times of clicking each commodity, the times of placing orders, the commodity clicking rate, the commodity visiting rate and the like of the user in a period of time; the attribute information of the user specifically includes: the gender of the user, whether the user is a new user, the frequent premises of the user, the red envelope sensitivity of the user, whether the user is a white collar or a student, etc.
The interactive information when the user accesses the merchant comprises the following steps: and exchanging traffic statistical information and price information.
It should be noted that the interactive traffic statistical information specifically includes: the times of clicking each commodity of the merchant, the times of placing an order on the commodity, the click rate of clicking the commodity of the merchant by the user, the rate of visiting and purchasing commodities and the like are determined within a period of time by the user; the price information specifically includes: the difference between the customer unit price of the user and the customer unit price of the merchant, and the like.
The context information when the user accesses the merchant includes: access information, delivery information, and network information.
It should be noted that the access information refers to a date on which the user specifically accesses the merchant, the distribution information refers to a distance that the merchant passes when distributing the goods placed by the user, a time and a weather condition on the day of the distribution, and the network information includes a network type when the user accesses the merchant, a device type used by the user, and the like.
At least one of information of a merchant to which the commodity belongs, interactive information when the user visits the merchant, information of the user and context information when the user visits the merchant is input into the data model, and a second characteristic factor of the merchant to which the commodity belongs is determined.
It should be noted that the data model is a data model established by using an Extreme gradient Boosting (XGBoost) algorithm, and the Boosting method is a method for improving the accuracy of the weak classification algorithm. The XGboost algorithm is one of boosting algorithms and is a tree model, so that a plurality of tree models are integrated together to form a strong classifier.
In a specific implementation, at least one of information of a merchant to which a commodity belongs, interactive information when a user accesses the merchant, information of the user and context information when the user accesses the merchant is input into an XGBoost data model, and a second characteristic factor of the merchant to which the commodity belongs can be calculated and obtained through real-time data of an online platform, wherein the second characteristic factor represents an information score of the merchant, and the specific information of the merchant is represented according to the information score of the merchant.
In step 103, a score value of the commodity is determined according to the first characteristic factor and the second characteristic factor.
It should be noted that, the score value of the product is determined according to the first characteristic factor capable of reflecting the user's preference degree of the product and the second characteristic factor capable of reflecting the user's evaluation of the merchant to which the product belongs, so that the user's preference degree and the types of users who enjoy the product can be comprehensively evaluated.
In a specific implementation, a comprehensive evaluation factor is obtained by comprehensively considering a first characteristic factor (a linkage factor of the commodity) and a second characteristic factor (an information score of the merchant), a specific score value of the commodity on a platform is specifically determined according to the comprehensive evaluation factor, the commodity is specifically evaluated by using the score value, and comprehensive evaluation of the commodity from the perspective of a user can be embodied by using the score value.
In step 104, the items are sorted according to their score values.
In a specific implementation, the score values of the commodities are collected, and the score values are arranged in an ascending order or a descending order to obtain the corresponding ordering number of the commodities in the ordering, that is, the ordering order of the commodities at the angle of the user can be obtained, so that the platform can determine the trend of which commodity is selected by the user according to the score values when the user consumes the commodities.
In the embodiment, aiming at different activity scenes, the first characteristic factors are flexibly configured and combined with the second characteristic factors to determine the score value of the commodity, the commodity intelligent sequencing model with different activity themes can be rapidly built, and then the commodity is sequenced through the score value to realize the commodity display of thousands of people and thousands of faces, so that accurate recommendation is provided for users, the commodity intelligent sequencing model is closer to user requirements, and further the experience degree of the users is improved.
A second embodiment of the present invention relates to a sorting method. The second embodiment is substantially the same as the first embodiment, and mainly differs therefrom in that: and performing weighted fusion on the first characteristic number and the second characteristic number, specifically determining the score value of the commodity, and sequencing the commodity according to the score value.
The specific processing flow is shown in fig. 2, in the present embodiment, the sorting method includes steps 201 to 206, because steps 201 to 202 in the present embodiment are the same as steps 101 to 102 in the first embodiment, and step 206 in the present embodiment is the same as step 104 in the first embodiment, which is not described herein again, and steps 203 to 205 in the present embodiment are described in detail below.
In step 203, the commodities are sorted according to the first characteristic factor, and a first characteristic number of the commodity is determined.
Wherein, the number of the recalled commodities is obtained; and determining a first characteristic number of the commodity according to the first characteristic factor and the number of the recalled commodities.
In one specific implementation, the number of the recalled commodities is divided by the first characteristic factor to obtain a quotient, the commodities are sorted according to the quotient to obtain sorting numbers corresponding to the commodities in the sorting, and the sorting number corresponding to each commodity is used as the first characteristic number of the commodity.
In step 204, the merchants to which the commodities belong are sorted according to the second characteristic factor, and a second characteristic number of the merchant is determined.
In a specific implementation, the second feature factor represents the information score of the merchant, the merchants are arranged in a descending order or an ascending order according to the information score of the merchant, after the ordering is completed, each merchant obtains a corresponding ordering number, and the ordering number is used as the second feature number of the merchant.
In step 205, the first feature number and the second feature number are weighted and fused to obtain a score value of the product.
Firstly, determining a weight corresponding to the first feature number and a weight corresponding to the second feature number; then, calculating the product of the weight corresponding to the first feature number and the first feature number to obtain a first product value; calculating the product of the weight corresponding to the second feature number and the second feature number to obtain a second product value; and finally, calculating the sum of the first product value and the second product value, and taking the obtained sum as the score value of the commodity.
In a specific implementation, the weight corresponding to the first feature number and the weight corresponding to the second feature number are determined according to the following steps: determining weights of the first characteristic factor and the second characteristic factor; and determining a weight corresponding to the first feature number and a weight corresponding to the second feature number according to the weights.
The weight herein refers to the degree of importance of a certain factor or index relative to a certain event, and it is emphasized that the relative degree of importance of the factor or index tends to be the degree of contribution or importance. Specifically, in the process of calculating the score value of the product, the weight represents which specific feature factor has a high contribution degree or importance degree, if the importance degree of the first feature factor (the linkage factor of the product) is high, the weight corresponding to the first feature number is large, otherwise, if the importance degree of the second feature factor (the information score of the merchant) is high, the weight corresponding to the second feature number is large. The sum of the weight corresponding to the first feature number and the weight corresponding to the second feature number is 1, and according to the difference of the weights, the ordering of the commodities can better reflect the requirements of the user, the requirements of different activity scenes can be flexibly adapted, accurate recommendation is provided for the user, and the user can be more close to the requirements of the user.
In one specific implementation, the ratio of the first characteristic factor to the second characteristic factor is set artificially, and the corresponding target ratio with the highest purchase return rate is used as the weight of the first characteristic factor and the second characteristic factor. The purchase return rate indicates a probability that the user purchases the product again after a certain period of time elapses after purchasing the product for the first time. For example, the ratio of the first characteristic factor to the second characteristic factor is set to be 0.1 and 0.9, or 0.5 and 0.5, or 0.8 and 0.2, respectively, and the buyback rate of the commodity in the preset time period is counted to be 10%, 50%, and 30% respectively according to different ratios, wherein the preset time period may be a week, a month, or other different time lengths. From the above-mentioned buyback rate, the buyback rate of the product is highest when the ratio of the first characteristic factor to the second characteristic factor is 0.5 to 0.5. Therefore, the weights of the first feature factor and the second feature factor are 0.5 and 0.5, that is, the importance degrees of the first feature factor and the second feature factor when evaluating the score value of the product are the same, the weight corresponding to the first feature number is 0.5, and the weight corresponding to the first feature number is also 0.5.
In a specific implementation, the server randomly determines the occupation ratio of the first characteristic factor and the second characteristic factor, where the occupation ratio may be any value greater than or equal to 0 and less than or equal to 1, and counts the purchase back rate of the commodity in a preset time period corresponding to different occupation ratios, where the purchase back rate may be represented by a percentage; and then, sequencing the purchase back rate, and determining the weight of the first characteristic factor and the second characteristic factor according to the ratio corresponding to the highest purchase back rate. For example, if the highest purchase back rate is 80%, and the corresponding occupation ratio is 4:6, the weights of the first feature factor and the second feature factor are 0.4:0.6, it is determined that the weight corresponding to the first feature number is 0.4, the weight corresponding to the first feature number is 0.6, and it should be noted that the sum of the weight corresponding to the first feature number and the weight corresponding to the first feature number is equal to 1.
In one particular implementation, the score value for the commodity may be obtained according to the following formula:
wherein,
score represents the Score value of the commodity, and N is the number of recalled commodities;
x represents the formulaGinseng is added;
when x is equal to i, wherein i represents any value in a value range from 0 to f, and f represents the number of the linkage factors forming the first characteristic factor; sort (Sort)index(i) The method comprises the steps that commodities are sequenced according to each linkage factor in first characteristic factors, and sequencing numbers corresponding to the commodities are obtained;representing a first feature number obtained from the first feature factor;
for example: when f is 4, the first characteristic factor is composed of 4 linkage factors, namely the monthly sales volume of the commodity, the good appraisal rate of the commodity, the delivery time corresponding to the commodity and the price information corresponding to the commodity; wherein i takes four values of 0, 1, 2 and 3 to calculate SR (i), and then SR (0), SR (1), SR (2) and SR (3) are added to obtain a first feature number
When x is equal to XGBoost(s), sortindex(xgboost (s)) means that the commodities are sorted according to the second characteristic factor, and the sorting number corresponding to the commodity is obtained; SR (xgboost (s)) represents a second feature number obtained from the second feature factor; wherein s represents at least one of information of a merchant to which the commodity belongs, interactive information when the user accesses the merchant, information of the user, and context information when the user accesses the merchant; inputting s into an XGboost(s) data model, and calculating and obtaining a second characteristic number SR (XGboost (s)) of a merchant to which the commodity belongs by using real-time data of an online platform;
α represents the weight corresponding to the first feature number, β represents the weight corresponding to the second feature number;
for example, when α is 0.5 and β is 0.5, it indicates that the importance of both the first and second feature factors in evaluating the score value of the product is 0.5, i.e., it is equally important;
the commodity score is obtained according to the formula, then the commodities are sorted according to the score corresponding to each commodity, and a final sorting result is obtained, so that a merchant can determine whether the corresponding commodity is favored by the user according to the sorting result, and further, targeted improvement is performed on the commodities disliked by the user, and the commercial tenant improves the operation effect.
In the embodiment, the weight corresponding to the first feature number and the weight corresponding to the second feature number are determined through the weights of the first feature factor and the second feature factor, the corresponding weights are used for weighting and fusing the first feature number and the second feature number, and the score value of the commodity is specifically determined, so that the obtained score value of the commodity can reflect the favorite degree of the commodity in different dimensions of the user, and a merchant can pertinently improve the corresponding commodity according to the score value, so that the commodity can adapt to the specific requirements of different recommended scenes.
The steps of the above methods are divided for clarity, and the implementation may be combined into one step or split some steps, and the steps are divided into multiple steps, so long as the same logical relationship is included, which are all within the protection scope of the present patent; it is within the scope of the patent to add insignificant modifications to the algorithms or processes or to introduce insignificant design changes to the core design without changing the algorithms or processes.
The third embodiment of the present invention relates to a sorting apparatus, and the specific implementation of the apparatus can be referred to the related description of the first embodiment, and repeated descriptions are omitted. It should be noted that, the specific implementation of the apparatus in this embodiment may also refer to the related description of the second embodiment, but is not limited to the above two examples, and other unexplained examples are also within the protection scope of the apparatus.
As shown in fig. 3, the sorting apparatus mainly includes: a first characteristic module 301, configured to determine a first characteristic factor according to information of a commodity, where the commodity corresponds to an activity scene; a second feature module 302, configured to determine a second feature factor according to at least one of information of a merchant to which the commodity belongs, information of the user, and association information between the merchant and the user; the commodity score module 303 is configured to determine a score value of the commodity according to the first characteristic factor and the second characteristic factor; and the sorting module 304 is used for sorting the commodities according to the score values of the commodities.
In one example, the item score module 303 is specifically configured to: sorting the commodities according to the first characteristic factor, and determining a first characteristic number of the commodities; sorting the commercial tenants to which the commodities belong according to the second characteristic factors, and determining second characteristic numbers of the commercial tenants; and performing weighted fusion on the first characteristic number and the second characteristic number to obtain the score value of the commodity.
In one example, the weighting and fusing the first feature number and the second feature number to obtain the score value of the product includes: determining a weight corresponding to the first feature number and a weight corresponding to the second feature number; calculating the product of the weight corresponding to the first feature number and the first feature number to obtain a first product value; calculating the product of the weight corresponding to the second feature number and the second feature number to obtain a second product value; the sum of the first product value and the second product value is calculated, and the resultant sum value is used as the score value of the commodity.
In one example, determining the weight corresponding to the first feature number and the weight corresponding to the second feature number includes: determining weights of the first characteristic factor and the second characteristic factor; and determining a weight corresponding to the first feature number and a weight corresponding to the second feature number according to the weights.
In an example, the second feature module 302 is specifically configured to: inputting at least one of information of a merchant to which the commodity belongs, interactive information when the user accesses the merchant, information of the user and context information when the user accesses the merchant into the data model, and determining a second characteristic factor of the merchant to which the commodity belongs.
In one example, the goods are sorted according to the first characteristic factor, and the determining the first characteristic number of the goods comprises: obtaining the number of recalled commodities; and determining a first characteristic number of the commodity according to the first characteristic factor and the number of the recalled commodities.
In one example, the information of the merchant to which the goods belong includes: the flow statistical information of the commercial tenant and the attribute information of the commercial tenant.
In one example, the information of the user includes: traffic statistics information of the user and attribute information of the user.
In one example, the interaction information when the user visits the merchant includes: interactive traffic statistics and pricing information; the context information when the user visits the merchant includes: access information, delivery information, and network information.
In one example, the information for the article includes: at least one item of monthly sales of the commodities, good appraisal rate of the commodities, delivery time corresponding to the commodities and price information corresponding to the commodities.
It should be understood that this embodiment is an example of the apparatus corresponding to the first or second embodiment, and may be implemented in cooperation with the first or second embodiment. The related technical details mentioned in the first or second embodiment are still valid in this embodiment, and are not described herein again in order to reduce repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first or second embodiment.
It should be noted that each module referred to in this embodiment is a logical module, and in practical applications, one logical unit may be one physical unit, may be a part of one physical unit, and may be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, elements that are not so closely related to solving the technical problems proposed by the present invention are not introduced in the present embodiment, but this does not indicate that other elements are not present in the present embodiment.
In the embodiment, aiming at different activity scenes, the first characteristic factors are flexibly configured and the second characteristic factors are combined to determine the score value of the commodity, so that intelligent commodity sorting models with different activity themes can be rapidly built, and then the commodities are sorted through the score value, so that the commodity display of thousands of people is realized, accurate recommendation is provided for users, the commodity display is closer to user requirements, and the user experience is further improved.
A fourth embodiment of the present invention relates to an electronic apparatus, as shown in fig. 4, including: at least one processor 401; and a memory 402 communicatively coupled to the at least one processor 401; wherein the memory 402 stores instructions executable by the at least one processor 401 to perform, by the at least one processor 401: determining a first characteristic factor according to the information of the commodity, wherein the commodity corresponds to the activity scene; determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of the user and association information between the merchant and the user; determining the score value of the commodity according to the first characteristic factor and the second characteristic factor; and sorting the commodities according to the score values of the commodities.
Specifically, the electronic device includes: one or more processors 401 and a memory 402, one processor 401 being exemplified in fig. 4. The processor 401 and the memory 402 may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example. Memory 402, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 401 executes various functional applications of the device and data processing by executing nonvolatile software programs, instructions, and modules stored in the memory 402, that is, implements the above-described sorting method.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 402 may optionally include memory 402 located remotely from the processor 401, and these remote memories 402 may be connected to external devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 402 and, when executed by the one or more processors 401, perform the ranking method of any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.
A fifth embodiment of the invention relates to a non-volatile storage medium for storing a computer-readable program for causing a computer to perform some or all of the above method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
The embodiment of the application discloses A1. a sorting method, which is characterized by comprising the following steps:
determining a first characteristic factor according to information of a commodity, wherein the commodity corresponds to an activity scene;
determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user and correlation information between the merchant and the user;
determining the score value of the commodity according to the first characteristic factor and the second characteristic factor;
and sorting the commodities according to the score values of the commodities.
A2. The sorting method according to a1, wherein determining the score value of the commodity according to the first characteristic factor and the second characteristic factor comprises:
sequencing the commodities according to the first characteristic factor, and determining a first characteristic number of the commodities;
sorting the commercial tenants to which the commodities belong according to the second characteristic factors, and determining second characteristic numbers of the commercial tenants;
and performing weighted fusion on the first characteristic number and the second characteristic number to obtain the score value of the commodity.
A3. The sorting method according to a2, wherein weighting and fusing the first feature number and the second feature number to obtain a score value of the product, comprises:
determining a weight corresponding to the first feature number and a weight corresponding to the second feature number;
calculating the product of the weight corresponding to the first feature number and the first feature number to obtain a first product value;
calculating the product of the weight corresponding to the second feature number and the second feature number to obtain a second product value;
and calculating the sum of the first product value and the second product value, and taking the obtained sum value as the score value of the commodity.
A4. The sorting method according to a3, wherein determining the weight corresponding to the first feature number and the weight corresponding to the second feature number includes:
determining weights of the first and second characteristic factors;
and determining a weight corresponding to the first feature number and a weight corresponding to the second feature number according to the weight.
A5. The ranking method according to any one of a1 to a4, wherein the association information between the merchant and the user includes:
the interaction information of the user and the merchant and the context information when the user accesses the merchant;
determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user, and association information between the merchant and the user, wherein the determining of the second characteristic factor comprises the following steps:
inputting at least one of information of a merchant to which the commodity belongs, interaction information of the user and the merchant, information of the user and context information when the user visits the merchant into a data model, and determining a second characteristic factor of the merchant to which the commodity belongs.
A6. The sorting method according to a2, wherein sorting the commodities according to the first characteristic factor and determining the first characteristic number of the commodity comprises:
obtaining the number of the recalled commodities;
and determining a first characteristic number of the commodity according to the first characteristic factor and the number of the recalled commodities.
A7. The sorting method according to a5, wherein the information of the merchant to which the commodity belongs includes: the flow statistical information of the commercial tenant and the attribute information of the commercial tenant.
A8. The sorting method according to A5, wherein the user information includes: the traffic statistics information of the user and the attribute information of the user.
A9. The sorting method according to A5, wherein the interaction information when the user visits the merchant includes: interactive traffic statistics and pricing information;
the context information of the user when accessing the merchant includes: access information, delivery information, and network information.
A10. The sorting method according to a1, wherein the information about the commodity includes:
at least one of a monthly sales volume of the commodity, a good appraisal rate of the commodity, a delivery time corresponding to the commodity, and price information corresponding to the commodity.
The embodiment of the application discloses B1 ordering device, its characterized in that includes:
the system comprises a first characteristic module, a second characteristic module and a third characteristic module, wherein the first characteristic module is used for determining a first characteristic factor according to information of commodities, and the commodities correspond to an activity scene;
the second characteristic module is used for determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user and correlation information between the merchant and the user;
the commodity score module is used for determining the score value of the commodity according to the first characteristic factor and the second characteristic factor;
and the sequencing module is used for sequencing the commodities according to the score values of the commodities.
The embodiment of the application discloses C1. electronic equipment, including memory and treater, the memory stores computer program, and the treater carries out when running program:
determining a first characteristic factor according to information of a commodity, wherein the commodity corresponds to an activity scene;
determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user and correlation information between the merchant and the user;
determining the score value of the commodity according to the first characteristic factor and the second characteristic factor;
and sorting the commodities according to the score values of the commodities.
C2. The electronic device of C1, wherein determining the score value of the item according to the first characteristic factor and the second characteristic factor comprises:
sequencing the commodities according to the first characteristic factor, and determining a first characteristic number of the commodities;
sorting the commercial tenants to which the commodities belong according to the second characteristic factors, and determining second characteristic numbers of the commercial tenants;
and performing weighted fusion on the first characteristic number and the second characteristic number to obtain the score value of the commodity.
C3. The electronic device according to C2, wherein the obtaining of the score value of the product by performing weighted fusion on the first feature number and the second feature number includes:
determining a weight corresponding to the first feature number and a weight corresponding to the second feature number;
calculating the product of the weight corresponding to the first feature number and the first feature number to obtain a first product value;
calculating the product of the weight corresponding to the second feature number and the second feature number to obtain a second product value;
and calculating the sum of the first product value and the second product value, and taking the obtained sum value as the score value of the commodity.
C4. The electronic device according to C3, wherein determining the weight corresponding to the first feature number and the weight corresponding to the second feature number includes:
determining weights of the first and second characteristic factors;
and determining a weight corresponding to the first feature number and a weight corresponding to the second feature number according to the weight.
C5. The electronic device according to any one of C1-C4, wherein the association information between the merchant and the user includes:
the interaction information of the user and the merchant and the context information when the user accesses the merchant;
determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user, and association information between the merchant and the user, wherein the determining of the second characteristic factor comprises the following steps:
inputting at least one of information of a merchant to which the commodity belongs, interaction information of the user and the merchant, information of the user and context information when the user visits the merchant into a data model, and determining a second characteristic factor of the merchant to which the commodity belongs.
C6. The electronic device according to C2, wherein the sorting the products according to the first characteristic factor and determining the first characteristic number of the product includes:
obtaining the number of the recalled commodities;
and determining a first characteristic number of the commodity according to the first characteristic factor and the number of the recalled commodities.
C7. The electronic device according to C5, wherein the information about the merchant to which the merchandise belongs includes: the flow statistical information of the commercial tenant and the attribute information of the commercial tenant.
C8. The electronic device according to C5, wherein the information of the user includes: the traffic statistics information of the user and the attribute information of the user.
C9. The electronic device according to C5, wherein the interaction information when the user accesses the merchant includes: interactive traffic statistics and pricing information;
the context information of the user when accessing the merchant includes: access information, delivery information, and network information.
C10. The electronic device according to C1, wherein the information about the commodity includes:
at least one of a monthly sales volume of the commodity, a good appraisal rate of the commodity, a delivery time corresponding to the commodity, and price information corresponding to the commodity.
A non-volatile storage medium storing a computer-readable program for causing a computer to perform the sorting method of any one of a 1-a 10 is disclosed in an embodiment of the present application.
Claims (10)
1. A method of sorting, the method comprising:
determining a first characteristic factor according to information of a commodity, wherein the commodity corresponds to an activity scene;
determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user and correlation information between the merchant and the user;
determining the score value of the commodity according to the first characteristic factor and the second characteristic factor;
and sorting the commodities according to the score values of the commodities.
2. The method of claim 1, wherein determining the score value for the item based on the first and second characteristic factors comprises:
sequencing the commodities according to the first characteristic factor, and determining a first characteristic number of the commodities;
sorting the commercial tenants to which the commodities belong according to the second characteristic factors, and determining second characteristic numbers of the commercial tenants;
and performing weighted fusion on the first characteristic number and the second characteristic number to obtain the score value of the commodity.
3. The sorting method according to claim 2, wherein the weighting and fusing the first feature number and the second feature number to obtain the score value of the product comprises:
determining a weight corresponding to the first feature number and a weight corresponding to the second feature number;
calculating the product of the weight corresponding to the first feature number and the first feature number to obtain a first product value;
calculating the product of the weight corresponding to the second feature number and the second feature number to obtain a second product value;
and calculating the sum of the first product value and the second product value, and taking the obtained sum value as the score value of the commodity.
4. The sorting method according to claim 3, wherein determining the weight corresponding to the first feature number and the weight corresponding to the second feature number comprises:
determining weights of the first and second characteristic factors;
and determining a weight corresponding to the first feature number and a weight corresponding to the second feature number according to the weight.
5. The ranking method according to any one of claims 1 to 4, wherein the association information between the merchant and the user includes:
the interaction information of the user and the merchant and the context information when the user accesses the merchant;
determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user, and association information between the merchant and the user, wherein the determining of the second characteristic factor comprises the following steps:
inputting at least one of information of a merchant to which the commodity belongs, interaction information of the user and the merchant, information of the user and context information when the user visits the merchant into a data model, and determining a second characteristic factor of the merchant to which the commodity belongs.
6. The sorting method according to claim 2, wherein sorting the articles according to the first feature factor, determining a first feature number of the article, comprises:
obtaining the number of the recalled commodities;
and determining a first characteristic number of the commodity according to the first characteristic factor and the number of the recalled commodities.
7. The sorting method according to claim 5, wherein the information of the merchant to which the commodity belongs comprises: the flow statistical information of the commercial tenant and the attribute information of the commercial tenant.
8. A sequencing apparatus, comprising:
the system comprises a first characteristic module, a second characteristic module and a third characteristic module, wherein the first characteristic module is used for determining a first characteristic factor according to information of commodities, and the commodities correspond to an activity scene;
the second characteristic module is used for determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user and correlation information between the merchant and the user;
the commodity score module is used for determining the score value of the commodity according to the first characteristic factor and the second characteristic factor;
and the sequencing module is used for sequencing the commodities according to the score values of the commodities.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor executing the program to perform:
determining a first characteristic factor according to information of a commodity, wherein the commodity corresponds to an activity scene;
determining a second characteristic factor according to at least one of information of a merchant to which the commodity belongs, information of a user and correlation information between the merchant and the user;
determining the score value of the commodity according to the first characteristic factor and the second characteristic factor;
and sorting the commodities according to the score values of the commodities.
10. A non-volatile storage medium storing a computer-readable program for causing a computer to perform the sorting method according to any one of claims 1 to 7.
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