CN108388464B - Advanced classification retrieval method based on local refreshing - Google Patents
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Abstract
The invention relates to a high-grade classified retrieval method based on local refreshing, wherein labels of high-grade classification of commodities are stored in an erasable and coverable mode through sessions in python, so that label retrieval and replacement can be realized for many times, instead of one-time irreplaceable retrieval, a search result of a fine classification condition generated by multiple sessions is subjected to intersection operation with the original commodity search, the result is fed back to the same front end, and the fed back front end page and the previous front end page are one page and only the retrieval result and the corresponding label display are locally changed.
Description
Technical Field
The invention relates to a high-level classification retrieval method based on local refreshing, and belongs to the technical field of computers.
Background
With the development of the times, the living standard of people is improved, and the shopping mode of people is changed. At present, online shopping becomes fashionable, and compared with the traditional shopping mode, the online shopping has many advantages, such as convenience, rapidness, complete types, time saving and labor saving. In order to provide better experience for users, an advanced classification retrieval module with excellent performance has the value and significance of the existence of the module.
At present, most of the ubiquitous advanced classification retrieval modules are implemented by operating on multiple pages, rather than local refreshing, creating different search interfaces according to different search conditions, and adding classified tags in a retrieval database early, which causes waste of resources and memory, and requires multiple pages to be switched continuously, and when the types of the classified tags are extremely large, it takes much time to construct a presentation interface. Therefore, a functional module complete advanced classification algorithm based on partial refreshing has the value and significance of the existing functional module.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a high-level classification retrieval method based on local refreshing;
the invention is similar to the advanced classification retrieval on the existing mainstream shopping website, and designs a multi-level classification retrieval functional module by using the advanced classification retrieval algorithm based on local refreshing designed by the user. The user can be according to different labels to filter the product of oneself needs, say that the brand of air conditioner is strong, beautiful etc. and say that the efficiency of air conditioner has one grade again, second grade etc. again, and the arrangement mode of air conditioner has the wardrobe formula, wall-hanging etc. both can filter according to a label classification, also supports multiple label to use the screening jointly, this very big improvement the performance of system. And the local refreshing is helpful to the user for improving the use experience and the system efficiency to a great extent.
The technical scheme of the invention is as follows:
an advanced classification retrieval method based on local refreshing comprises the following steps:
(1) collecting advanced classification labels of commodities by using an automatic processing program, and storing the advanced classification labels into a database; the database comprises a user data table, a commodity data table and a user commodity association table; the user data table comprises basic information of the user, and the basic information of the user comprises the name, the telephone, the mailbox and the identity card number of the user; the commodity data table comprises the name, price, sales volume, evaluation score, purchase link and three classification labels of the commodity; the user commodity association table comprises the main key information of the user and the commodity; the three types of labels are commodity labels extracted from various types of commodity information, and are different for each type of commodity, for example, for a refrigerator product, the labels of the three types of labels are a door opening mode, a refrigeration mode and an energy efficiency grade; for television, its labels would be size, definition, and energy efficiency ratings. The main key information of the user and the commodity is the only index information for distinguishing the user from the commodity, and each user and each commodity have the only main key information.
(2) Initializing a value in a corresponding session in the high-level classification label, namely setting the value in the corresponding session in the high-level classification label to be null; namely: the sessions are used for storing attributes and configuration information required by a specific user session, when a session is enabled for the first time, a unique identifier is stored in a local cookie, three types of tags are created in the session, and initial values of the three types of tags are all set to be null.
(3) Monitoring a common search function, and acquiring advanced classification labels of all commodities in a search process;
(4) monitoring the click of a user, and when a certain commodity obtained in the step (3) is clicked, obtaining a value a in the session corresponding to the high-level classification label of the commodity and feeding back the value a to the rear end; i.e. the post method of HTTP is used to transfer the advanced category labels clicked by the user to the background.
(5) Judging whether a value in the session corresponding to the high-level classification label monitored in the step (4) is empty, if so, entering a step (7), otherwise, entering a step (6);
(6) replacing the value in the session corresponding to the high-level classification label monitored in the step (4) with a; a refers to a value that is imported by the post method of HTTP.
(7) Constructing a search result according to conditions in all sessions and the large search category, wherein the conditions in the sessions refer to the three types of tags created in the sessions in the step (2); the large search category refers to: a search is made according to the title of the item, which is not classified in detail, listing all the products belonging to that title.
According to the invention, the step (1) of collecting the advanced classification labels of the commodities by using an automatic processing program comprises the following steps:
A. crawling the classification labels of the commodities on the Jingdong and Taobao websites by adopting a Scapy crawler with a Python structure;
B. and D, classifying the classification labels of the commodities crawled in the step A, and collecting the advanced classification labels of the commodities. Ensuring that each high-level category label for a given item is unique; the advanced classification labels are relatively special labels of the commodities, and are different from labels of other commodities in the important attributes of the commodities, such as the energy efficiency index and the arrangement mode of an air conditioner.
The invention stores the label of commodity high-class classification in an erasable and coverable mode through the session in python, thus realizing the label retrieval and replacement for many times, but not performing the irreplaceable retrieval for one time, performing an intersection operation on the search result of the subclassification condition generated by the multiclass session and the original commodity search, and feeding back the result to the same front end, wherein the fed-back front end page and the front end page are one, and only locally changing the retrieval result and the corresponding label display.
According to a preferred embodiment of the present invention, in step (2), three types of tags are created in session, which means: and continuously transmitting new parameters through a post method of the HTTP, judging which type of label the parameter belongs to in the background, and writing the corresponding session.
Thus, when a user jumps between Web pages of an application, the variables stored in the session will not be lost, but will persist throughout the user session. When a user requests a Web page from an application, the Web server will automatically create a session object if the user has not already had a session. When a session expires or is abandoned, the server will terminate the session.
According to the preferable selection of the invention, in the step (3), the common search function is monitored, the advanced classification labels of all the commodities are obtained in the search process, the advanced classification labels of the commodities in the database are found out through the commodity titles obtained in the monitoring process, and the advanced classification labels of the commodities are displayed to the user on the front-end interface. The user can conveniently select the commodities with more detailed categories.
The invention has the beneficial effects that:
1. the search speed is improved by utilizing a query set cache mechanism;
2. locally changing the display of the retrieval result and the corresponding tag without requiring a plurality of front-end interface responses;
3. the use of the Q function allows for the encapsulation of key parameters to better apply multiple queries.
Drawings
FIG. 1 is a flow chart of an advanced classification search method according to the present invention;
FIG. 2 is a diagram of results from a search using a prior art algorithm;
FIG. 3 is a diagram illustrating the results of an advanced classification search method according to the present invention;
fig. 4 is a schematic diagram illustrating the effect of the advanced classification search method of the present invention for realizing advanced classification search of ultra-high definition 65-inch products of the video brand of the flat panel television.
Detailed Description
The invention is further defined in the following, but not limited to, the figures and examples in the description.
Example 1
An advanced classification retrieval method based on local refresh, as shown in fig. 1, includes:
(1) collecting advanced classification labels of commodities by using an automatic processing program, and storing the advanced classification labels into a database; the database adopts a MYSQL database, and comprises a user data table, a commodity data table and a user commodity association table; the user data table comprises basic information of the user, and the basic information of the user comprises the name, the telephone, the mailbox and the identity card number of the user; the commodity data table comprises the name, price, sales volume, evaluation score, purchase link and three classification labels of the commodity; the user commodity association table comprises the main key information of the user and the commodity; the three types of labels are commodity labels extracted from various types of commodity information, and are different for each type of commodity, for example, for a refrigerator product, the labels of the three types of labels are a door opening mode, a refrigeration mode and an energy efficiency grade; for television, its labels would be size, definition, and energy efficiency ratings. The main key information of the user and the commodity is the only index information for distinguishing the user from the commodity, and each user and each commodity have the only main key information.
(2) Initializing a value in a corresponding session in the high-level classification label, namely setting the value in the corresponding session in the high-level classification label to be null; namely: the sessions are used for storing attributes and configuration information required by a specific user session, when a session is enabled for the first time, a unique identifier is stored in a local cookie, three types of tags are created in the session, and initial values of the three types of tags are all set to be null. Creating three types of tags in session means: and continuously transmitting new parameters through a post method of the HTTP, judging which type of label the parameter belongs to in the background, and writing the corresponding session.
Thus, when a user jumps between Web pages of an application, the variables stored in the session will not be lost, but will persist throughout the user session. When a user requests a Web page from an application, the Web server will automatically create a session object if the user has not already had a session. When a session expires or is abandoned, the server will terminate the session.
(3) Monitoring a common search function, and acquiring advanced classification labels of all commodities in a search process; the method comprises the following steps: and monitoring a common search function, acquiring the advanced classification labels of all commodities in the search process, finding out the advanced classification labels of the commodities in the database through the commodity titles acquired in the monitoring process, and displaying the advanced classification labels of the commodities to a user on a front-end interface. The user can conveniently select the commodities with more detailed categories.
(4) Monitoring the click of a user, and when a certain commodity obtained in the step (3) is clicked, obtaining a value a in the session corresponding to the high-level classification label of the commodity and feeding back the value a to the rear end; i.e. the post method of HTTP is used to transfer the advanced category labels clicked by the user to the background.
(5) Judging whether a value in the session corresponding to the high-level classification label monitored in the step (4) is empty, if so, entering a step (7), otherwise, entering a step (6);
(6) replacing the value in the session corresponding to the high-level classification label monitored in the step (4) with a; a refers to a value that is imported by the post method of HTTP.
(7) Constructing a search result according to conditions in all sessions and the large search category, wherein the conditions in the sessions refer to the three types of tags created in the sessions in the step (2); the large search category refers to: a search is made according to the title of the item, which is not classified in detail, listing all the products belonging to that title.
FIG. 2 is a diagram of results from a search using a prior art algorithm; FIG. 3 is a diagram illustrating the result of the advanced classification search method according to this embodiment; as can be seen from the comparison between fig. 2 and fig. 3, the total time for completing the search before the advanced classification search algorithm is used is 959ms, and the time for completing the loading of the website is 1.25 s. After use, the total time length for completing the search is 548ms, and the time for completing the website loading is 809 ms. Therefore, in the aspect of advanced classified search, the search efficiency is improved by one time, and in the aspect of integral loading of the website, the integral loading efficiency is improved by 0.5 time.
Fig. 4 is a schematic diagram illustrating the effect of implementing the advanced classification search of the ultra-high-definition 65-inch product of the video brand of the flat panel television by using the advanced classification search method of the present embodiment. By designing the high-level classification retrieval algorithm with local refreshing, the comparison which can be carried out by the user at present is not coarse, and the user can randomly select the commodities of a specific type or brand required by the user to carry out fine-grained comparison.
Example 2
According to the advanced classification retrieval method based on partial refresh described in embodiment 1, the difference is that,
the step (1) of collecting the advanced classification labels of the commodities by using an automatic processing program comprises the following steps:
A. crawling the classification labels of the commodities on the Jingdong and Taobao websites by adopting a Scapy crawler with a Python structure;
B. and D, classifying the classification labels of the commodities crawled in the step A, and collecting the advanced classification labels of the commodities. Ensuring that each high-level category label for a given item is unique; the advanced classification labels are relatively special labels of the commodities, and are different from labels of other commodities in the important attributes of the commodities, such as the energy efficiency index and the arrangement mode of an air conditioner.
The invention stores the label of commodity high-class classification in an erasable and coverable mode through the session in python, thus realizing the label retrieval and replacement for many times, but not performing the irreplaceable retrieval for one time, performing an intersection operation on the search result of the subclassification condition generated by the multiclass session and the original commodity search, and feeding back the result to the same front end, wherein the fed-back front end page and the front end page are one, and only locally changing the retrieval result and the corresponding label display.
Algorithm pseudocode
The pseudo code takes four types of high-level classification labels as an example, and shows the operation mechanism of the algorithm in detail, including data interaction at the front end and data processing at the background.
Claims (4)
1. An advanced classification retrieval method based on local refreshing is characterized by comprising the following steps:
(1) collecting advanced classification labels of commodities by using an automatic processing program, and storing the advanced classification labels into a database; the database comprises a user data table, a commodity data table and a user commodity association table; the user data table comprises basic information of the user, and the basic information of the user comprises the name, the telephone, the mailbox and the identity card number of the user; the commodity data table comprises the name, price, sales volume, evaluation score, purchase link and three classification labels of the commodity; the user commodity association table comprises the main key information of the user and the commodity;
(2) initializing a value in a corresponding session in the high-level classification label, namely setting the value in the corresponding session in the high-level classification label to be null; namely: the session is used for storing the attribute and configuration information required by a specific user session, when a session is started for the first time, a unique identifier is stored in a local cookie, three types of tags are created in the session, and the initial values of the three types of tags are all set to be null;
(3) monitoring a common search function, and acquiring advanced classification labels of all commodities in a search process;
(4) monitoring the click of a user, and when a certain commodity obtained in the step (3) is clicked, obtaining a value a in the session corresponding to the high-level classification label of the commodity and feeding back the value a to the rear end;
(5) judging whether a value in the session corresponding to the high-level classification label monitored in the step (4) is empty, if so, entering a step (7), otherwise, entering a step (6);
(6) replacing the value in the session corresponding to the high-level classification label monitored in the step (4) with a;
(7) constructing a search result according to conditions in all sessions and the large search category, wherein the conditions in the sessions refer to the three types of tags created in the sessions in the step (2); the large search category refers to: a search is performed according to the title of the product, listing all products belonging to the title.
2. The advanced classification retrieval method based on partial refresh as claimed in claim 1, wherein the step (1) of collecting advanced classification labels of commodities by using an automated processing program comprises:
A. crawling the classification labels of the commodities on the Jingdong and Taobao websites by adopting a Scapy crawler with a Python structure;
B. and D, classifying the classification labels of the commodities crawled in the step A, and collecting the advanced classification labels of the commodities.
3. The advanced classification retrieval method based on local refresh as claimed in claim 1, wherein said step (2) creates three types of labels in session, which means: and continuously transmitting new parameters through a post method of the HTTP, judging which type of label the parameter belongs to in the background, and writing the corresponding session.
4. The advanced classification retrieval method based on local refreshing as claimed in any one of claims 1-3, wherein in step (3), the normal search function is monitored, the advanced classification labels of all the commodities are obtained in the search process, the advanced classification labels of the commodities in the database are found out through the commodity titles obtained in the monitoring process, and the advanced classification labels of the commodities are displayed to the user on the front-end interface.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090001974A (en) * | 2007-05-30 | 2009-01-09 | 제주대학교 산학협력단 | System and method for sales forecasting by mobile rfid based statistical analysis |
| CN101563869A (en) * | 2006-12-22 | 2009-10-21 | 苹果公司 | Communicating and storing information associated with media broadcasts |
| CN102103606A (en) * | 2009-12-18 | 2011-06-22 | 北京闻言科技有限公司 | Technology for realizing advanced search of commodities on mobile phone client |
| CN102411754A (en) * | 2011-11-29 | 2012-04-11 | 南京大学 | Personalized recommendation method based on commodity property entropy |
| CN103020128A (en) * | 2012-11-19 | 2013-04-03 | 北京奇虎科技有限公司 | Method and device for data interaction with terminal device |
| CN103473273A (en) * | 2013-08-22 | 2013-12-25 | 百度在线网络技术（北京）有限公司 | Information search method, information search device and server |
| CN104123356A (en) * | 2014-07-17 | 2014-10-29 | 成都康赛信息技术有限公司 | Method for increasing webpage response speed under large data volume condition |
| CN106372090A (en) * | 2015-07-23 | 2017-02-01 | 苏宁云商集团股份有限公司 | Query clustering method and device |
| CN106372956A (en) * | 2015-07-23 | 2017-02-01 | 苏宁云商集团股份有限公司 | Method and system for intention entity recognition based on user query log |
| CN107577755A (en) * | 2017-08-31 | 2018-01-12 | 江西博瑞彤芸科技有限公司 | A kind of searching method |
| CN107679247A (en) * | 2017-10-31 | 2018-02-09 | 南威软件股份有限公司 | A kind of method that electric business website realizes self-defined maintenance items extension information |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8504550B2 (en) * | 2009-05-15 | 2013-08-06 | Citizennet Inc. | Social network message categorization systems and methods |
-
2018
- 2018-03-07 CN CN201810183959.1A patent/CN108388464B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101563869A (en) * | 2006-12-22 | 2009-10-21 | 苹果公司 | Communicating and storing information associated with media broadcasts |
| KR20090001974A (en) * | 2007-05-30 | 2009-01-09 | 제주대학교 산학협력단 | System and method for sales forecasting by mobile rfid based statistical analysis |
| CN102103606A (en) * | 2009-12-18 | 2011-06-22 | 北京闻言科技有限公司 | Technology for realizing advanced search of commodities on mobile phone client |
| CN102411754A (en) * | 2011-11-29 | 2012-04-11 | 南京大学 | Personalized recommendation method based on commodity property entropy |
| CN103020128A (en) * | 2012-11-19 | 2013-04-03 | 北京奇虎科技有限公司 | Method and device for data interaction with terminal device |
| CN103473273A (en) * | 2013-08-22 | 2013-12-25 | 百度在线网络技术（北京）有限公司 | Information search method, information search device and server |
| CN104123356A (en) * | 2014-07-17 | 2014-10-29 | 成都康赛信息技术有限公司 | Method for increasing webpage response speed under large data volume condition |
| CN106372090A (en) * | 2015-07-23 | 2017-02-01 | 苏宁云商集团股份有限公司 | Query clustering method and device |
| CN106372956A (en) * | 2015-07-23 | 2017-02-01 | 苏宁云商集团股份有限公司 | Method and system for intention entity recognition based on user query log |
| CN107577755A (en) * | 2017-08-31 | 2018-01-12 | 江西博瑞彤芸科技有限公司 | A kind of searching method |
| CN107679247A (en) * | 2017-10-31 | 2018-02-09 | 南威软件股份有限公司 | A kind of method that electric business website realizes self-defined maintenance items extension information |
Non-Patent Citations (2)
| Title |
|---|
| 《商品检索中的多任务识别与分析》;张鹏翼 等;《现代图书情报技术》;20160331;第2016年卷(第3期);第1-7页 * |
| 《面向智能信息检索的Web挖掘关键技术研究》;袁方;《中国博士学位论文全文数据库（电子期刊）信息科技辑》;20061115;第2006年卷(第11期);第I138-126页 * |
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