CN112184362A - Product searching fission pushing method and system based on big data and storage medium - Google Patents
Product searching fission pushing method and system based on big data and storage medium Download PDFInfo
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- CN112184362A CN112184362A CN202010923093.0A CN202010923093A CN112184362A CN 112184362 A CN112184362 A CN 112184362A CN 202010923093 A CN202010923093 A CN 202010923093A CN 112184362 A CN112184362 A CN 112184362A
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Abstract
The invention relates to a product searching fission pushing method based on big data, which comprises the following steps: acquiring registration information of a shopping platform set up by a user, and acquiring a behavior preference portrait log of the user according to the registration information; calculating a product object pushed in the first round according to the behavior preference portrait log of the user, and pushing the product object to the user; acquiring search intention keywords of a user, and performing information integration and information preprocessing on the search intention keywords and registration information to obtain an updated behavior preference portrait log of the user; calculating to obtain a product object pushed in the next round according to the updated behavior preference portrait log of the user, and pushing the product object to the user; repeatedly acquiring search intention keywords of the user, and pushing the product object according to the method until the user finishes searching. The invention can facilitate the user to find the target product better and faster, and optimizes the purchasing experience of the user.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a product searching fission pushing method and system based on big data and a storage medium.
Background
Enterprises or other sellers often have a psychological expectation product during the process of purchasing raw materials, and at the moment, the enterprises or other sellers need to go to a purchasing platform to compare prices and goods, and finally determine the self selection by comparing the cost performance of different products;
the general method is to carry out intention expression to customer service personnel, and the customer service personnel carry out targeted push according to the intention expression, but the cost for cultivating the customer service personnel is not low, once the customer service personnel leave the job, the training needs to be repeated, and when the number of inquired orders is large, the manual work is often difficult to deal with;
the current market needs a method for automatically pushing products to users according to behavior preferences of the users, which is convenient for the users to select the products and can save the related cost of the platform.
Disclosure of Invention
The invention aims to solve at least one of the defects of the prior art and provides a product search fission pushing method, a product search fission pushing system and a storage medium based on big data.
In order to achieve the purpose, the invention adopts the following technical scheme:
specifically, a product searching fission pushing method based on big data is provided, which comprises the following steps:
acquiring registration information of a shopping platform set up by a user, and acquiring a behavior preference portrait log of the user according to the registration information;
calculating a product object pushed in the first round according to the behavior preference portrait log of the user, and pushing the product object to the user;
acquiring search intention keywords of a user, and performing information integration and information preprocessing on the search intention keywords and registration information to obtain an updated behavior preference portrait log of the user;
calculating to obtain a product object pushed in the next round according to the updated behavior preference portrait log of the user, and pushing the product object to the user;
repeatedly acquiring search intention keywords of the user, and pushing the product object according to the method until the user finishes searching.
Further, the shopping platform for acquiring the registration information of the user is a material searching network platform.
Further, the user behavior preference profile log specifically includes a log header, an event description field, and an additional data field,
the log header includes the date, event, username, hostname event ID, record source, and event type on which the log was formed;
the event description field comprises a server name for storing registration information, a handle ID, a process ID, a main user name, a main domain, a user name, a user login ID and related update information of a user;
the additional data field comprises binary data displayed in a 16-ary manner generated by an application that generates the behavioral preference profile log.
Further, the pushing of the product object according to the behavior preference profile log of the user specifically includes the following steps:
acquiring a behavior preference portrait log of a user, and extracting description fields related to the user from the behavior preference portrait log to form a field set P, wherein P is { P1, P2., Pn } an item in which Pi is P, and 1 < i < n;
acquiring a set Q of products needing to be pushed, wherein Qi is a term of Q, and 1 < i < m; the association rule for establishing field set P with set Q is as follows,
p＝>q，
wherein
And p ═ q ═ phi;
calculating a support factor support (p ═ q) for the association rule p ═ q and a confidence factor confidence (p ═ q) for the association rule p ═ q, wherein
support(p＝>q)＝P(p U q)，confidence(p＝>q)＝P(p|q)
Finding out all frequent set subsets with a support factor support (p ═ q) larger than a first threshold value, and generating a new strong association rule through the frequent set subsets;
and pushing the product elements in the subset Q of the set Q of products in the new strong association rule to the user as target elements.
Further, the method further comprises the steps of timing the search interval time T of the user when the user searches for the first time, and performing information integration and information preprocessing by taking the search interval time T as an influence factor to obtain the behavior preference portrait log of the user.
Further, the operation of performing information integration and information preprocessing on the search intention keyword and the registration information specifically includes the following steps:
integrating the search intention keywords and the registration information in an oracle database through two non-real-time systems to obtain an integrated data set;
performing data cleaning on the integrated data set by using a drop function in Pandas;
and performing data dimension reduction processing on the data set subjected to data cleaning through linear decision analysis, and forming an updated behavior preference portrait log of the user.
The invention also provides a product searching fission push system based on big data, which comprises,
the system comprises a first operation module, a second operation module and a third operation module, wherein the first operation module is used for acquiring registration information of a shopping platform built by a user and acquiring a behavior preference portrait log of the user according to the registration information;
the second operation module is used for calculating a product object pushed in the first round according to the behavior preference portrait log of the user and pushing the product object to the user;
the third operation module is used for acquiring search intention keywords of the user, and performing information integration and information preprocessing on the search intention keywords and the registration information to obtain an updated behavior preference portrait log of the user;
the fourth operation module is used for calculating the image log according to the updated behavior preference of the user to obtain a product object pushed in the next round and pushing the product object to the user;
and the fifth operation module is used for repeatedly executing the third operation module and the fourth operation module until the user finishes searching.
The invention also proposes a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
The invention has the beneficial effects that:
according to the invention, the first target product is pushed to the user through the first registration information of the user on the shopping platform, the second round of target product pushing is carried out on the user in a targeted manner according to the search record feedback of the user until the user finishes searching, and the related record is stored to the cloud end as the first round of pushing scheme when the user uses the system for the next time, so that the user can find the target product better and faster, and the purchasing experience of the user is optimized.
Drawings
FIG. 1 is a flow chart of a big data-based product search fission pushing method according to the present invention;
FIG. 2 is a block diagram of a big data-based product search fission push system according to the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
Referring to fig. 1, in embodiment 1, the present invention provides a product search fission push method based on big data, including the following steps:
acquiring registration information of a shopping platform set up by a user, and acquiring a behavior preference portrait log of the user according to the registration information;
calculating a product object pushed in the first round according to the behavior preference portrait log of the user, and pushing the product object to the user;
acquiring search intention keywords of a user, and performing information integration and information preprocessing on the search intention keywords and registration information to obtain an updated behavior preference portrait log of the user;
calculating to obtain a product object pushed in the next round according to the updated behavior preference portrait log of the user, and pushing the product object to the user;
repeatedly acquiring search intention keywords of the user, and pushing the product object according to the method until the user finishes searching.
In embodiment 1, the method and the device can push the target product for the first time to the user through the first registration information of the user on the shopping platform, feed back the targeted push of the target product for the second time to the user according to the search record of the user until the user finishes searching, and store the related record to the cloud as the first-round pushing scheme when the user uses the product for the next time, so that the user can find the target product better and faster, and the shopping experience of the user is optimized.
As a preferred embodiment of the present invention, the shopping platform for acquiring the registration information of the user is a web searching platform. Since the web searching platform is a product owned by the company, the corresponding publication number CN108960970A of the related invention patent has also been applied, so the web searching platform is preferably used.
In a preferred embodiment of the present invention, the behavioral preference profile log of the user specifically includes a log header, an event description field, and an additional data field,
the log header includes the date, event, username, hostname event ID, record source, and event type on which the log was formed;
the event description field comprises a server name for storing registration information, a handle ID, a process ID, a main user name, a main domain, a user name, a user login ID and related update information of a user;
the additional data field comprises binary data displayed in a 16-ary manner generated by an application that generates the behavioral preference profile log.
As a preferred embodiment of the present invention, the pushing of the product object according to the behavioral preference profile log of the user specifically includes the following steps:
acquiring a behavior preference portrait log of a user, and extracting description fields related to the user from the behavior preference portrait log to form a field set P, wherein P is { P1, P2., Pn } an item in which Pi is P, and 1 < i < n;
acquiring a set Q of products needing to be pushed, wherein Qi is a term of Q, and 1 < i < m; the association rule for establishing field set P with set Q is as follows,
p＝>q，
wherein
And p ═ q ═ phi;
calculating a support factor support (p ═ q) for the association rule p ═ q and a confidence factor confidence (p ═ q) for the association rule p ═ q, wherein
support(p＝>q)＝P(p U q)，confidence(p＝>q)＝P(p|q)
Finding out all frequent set subsets with a support factor support (p ═ q) larger than a first threshold value, and generating a new strong association rule through the frequent set subsets;
and pushing the product elements in the subset Q of the set Q of products in the new strong association rule to the user as target elements.
The specific implementation of the relevant code for the frequent set subset is as follows:
in a preferred embodiment of the present invention, the method further includes, when the user performs the first search, timing the search interval T of the user, and performing information integration and information preprocessing using the search interval T as an influence factor to obtain the behavioral preference portrait log of the user.
Because the user often stays for a longer time for comparison and screening when searching the content corresponding to the interesting entry, the search time interval T can also be used as an important influence factor for final pushing, and the stock can integrate the time interval T into the behavior preference portrait log of the user
As a preferred embodiment of the present invention, the operation of integrating the search intention keyword and the registration information and preprocessing the information specifically includes the following steps:
integrating the search intention keywords and the registration information in an oracle database through two non-real-time systems to obtain an integrated data set;
performing data cleaning on the integrated data set by using a drop function in Pandas;
and performing data dimension reduction processing on the data set subjected to data cleaning through linear decision analysis, and forming an updated behavior preference portrait log of the user.
The following idea is adopted when information integration and information preprocessing are performed, wherein the related technologies are not mature technologies in the prior art, and therefore are not described in detail, and certainly, the invention can be performed in other ways and finally achieve the purpose of the invention.
Referring to fig. 2, the present invention further provides a big data based product search fission push system, including,
the system comprises a first operation module, a second operation module and a third operation module, wherein the first operation module is used for acquiring registration information of a shopping platform built by a user and acquiring a behavior preference portrait log of the user according to the registration information;
the second operation module is used for calculating a product object pushed in the first round according to the behavior preference portrait log of the user and pushing the product object to the user;
the third operation module is used for acquiring search intention keywords of the user, and performing information integration and information preprocessing on the search intention keywords and the registration information to obtain an updated behavior preference portrait log of the user;
the fourth operation module is used for calculating the image log according to the updated behavior preference of the user to obtain a product object pushed in the next round and pushing the product object to the user;
and the fifth operation module is used for repeatedly executing the third operation module and the fourth operation module until the user finishes searching.
The corresponding system can also achieve the related invention purpose when in operation, and particularly can push the first target product to the user through the first registration information of the user on the shopping platform, feed back the targeted push of the second round target product to the user according to the search record of the user until the user finishes searching, and store the related record to the cloud end as the first round push scheme when the user uses the system next time, so that the user can find the target product better and faster, and the shopping experience of the user is optimized.
The invention also proposes a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the above-described method embodiments when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
While the present invention has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed as effectively covering the intended scope of the invention by providing a broad, potential interpretation of such claims in view of the prior art with reference to the appended claims. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and the present invention shall fall within the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (8)
1. A product searching fission pushing method based on big data is characterized by comprising the following steps:
acquiring registration information of a shopping platform set up by a user, and acquiring a behavior preference portrait log of the user according to the registration information;
calculating a product object pushed in the first round according to the behavior preference portrait log of the user, and pushing the product object to the user;
acquiring search intention keywords of a user, and performing information integration and information preprocessing on the search intention keywords and registration information to obtain an updated behavior preference portrait log of the user;
calculating to obtain a product object pushed in the next round according to the updated behavior preference portrait log of the user, and pushing the product object to the user;
repeatedly acquiring search intention keywords of the user, and pushing the product object according to the method until the user finishes searching.
2. The big data-based product searching fission pushing method according to claim 1, wherein the shopping platform for obtaining the registration information of the user is a search web platform.
3. The big data based product search fission push method according to claim 2, wherein said user behavior preference profile log includes a log header, an event description field and an additional data field,
the log header includes the date, event, username, hostname event ID, record source, and event type on which the log was formed;
the event description field comprises a server name for storing registration information, a handle ID, a process ID, a main user name, a main domain, a user name, a user login ID and related update information of a user;
the additional data field comprises binary data displayed in a 16-ary manner generated by an application that generates the behavioral preference profile log.
4. The big data-based product searching fission pushing method according to claim 3, wherein the pushing of the product object according to the user behavior preference portrait log specifically comprises the following steps:
acquiring a behavior preference portrait log of a user, and extracting description fields related to the user from the behavior preference portrait log to form a field set P, wherein P is { P1, P2., Pn } an item in which Pi is P, and 1 < i < n;
acquiring a set Q of products needing to be pushed, wherein Qi is a term of Q, and 1 < i < m;
the association rule for establishing field set P with set Q is as follows,
p＝>q，
wherein
And p ═ q ═ phi;
calculating a support factor support (p ═ q) for the association rule p ═ q and a confidence factor confidence (p ═ q) for the association rule p ═ q, wherein
support(p＝>q)＝P(p U q)，confidence(p＝>q)＝P(p|q)
Finding out all frequent set subsets with a support factor support (p ═ q) larger than a first threshold value, and generating a new strong association rule through the frequent set subsets;
and pushing the product elements in the subset Q of the set Q of products in the new strong association rule to the user as target elements.
5. The big data-based product searching fission pushing method according to claim 1, further comprising timing a search interval T of the user when the user performs the first search, and performing information integration and information preprocessing on the search interval T as an influence factor to obtain a behavioral preference portrait log of the user.
6. The big data-based product search fission pushing method according to claim 1, wherein the operation of performing information integration and information preprocessing on the search intention keyword and the registration information specifically includes the following operations:
integrating the search intention keywords and the registration information in an oracle database through two non-real-time systems to obtain an integrated data set;
performing data cleaning on the integrated data set by using a drop function in Pandas;
and performing data dimension reduction processing on the data set subjected to data cleaning through linear decision analysis, and forming an updated behavior preference portrait log of the user.
7. A big data-based product search fission push system is characterized by comprising,
the system comprises a first operation module, a second operation module and a third operation module, wherein the first operation module is used for acquiring registration information of a shopping platform built by a user and acquiring a behavior preference portrait log of the user according to the registration information;
the second operation module is used for calculating a product object pushed in the first round according to the behavior preference portrait log of the user and pushing the product object to the user;
the third operation module is used for acquiring search intention keywords of the user, and performing information integration and information preprocessing on the search intention keywords and the registration information to obtain an updated behavior preference portrait log of the user;
the fourth operation module is used for calculating the image log according to the updated behavior preference of the user to obtain a product object pushed in the next round and pushing the product object to the user;
and the fifth operation module is used for repeatedly executing the third operation module and the fourth operation module until the user finishes searching.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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| CN202010923093.0A CN112184362A (en) | 2020-09-04 | 2020-09-04 | Product searching fission pushing method and system based on big data and storage medium |
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| CN202010923093.0A CN112184362A (en) | 2020-09-04 | 2020-09-04 | Product searching fission pushing method and system based on big data and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592540A (en) * | 2021-07-14 | 2021-11-02 | 车智互联（北京）科技有限公司 | User fission method and computing device |
| CN114756745A (en) * | 2022-03-29 | 2022-07-15 | 重庆义康鑫科技有限公司 | Intelligent information recommendation method and device based on big data analysis |
-
2020
- 2020-09-04 CN CN202010923093.0A patent/CN112184362A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592540A (en) * | 2021-07-14 | 2021-11-02 | 车智互联（北京）科技有限公司 | User fission method and computing device |
| CN113592540B (en) * | 2021-07-14 | 2023-09-19 | 车智互联（北京）科技有限公司 | User fission method and computing device |
| CN114756745A (en) * | 2022-03-29 | 2022-07-15 | 重庆义康鑫科技有限公司 | Intelligent information recommendation method and device based on big data analysis |
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Application publication date: 20210105 |