CN109636545A - A kind of electric business platform commercial product recommending algorithm - Google Patents
A kind of electric business platform commercial product recommending algorithm Download PDFInfo
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- CN109636545A CN109636545A CN201811603331.9A CN201811603331A CN109636545A CN 109636545 A CN109636545 A CN 109636545A CN 201811603331 A CN201811603331 A CN 201811603331A CN 109636545 A CN109636545 A CN 109636545A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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Abstract
The invention discloses a kind of electric business platform commercial product recommending algorithms, the following steps are included: step 1: obtaining operator's initial data: obtaining user and retrieval transcript from operator's big data system, comprising: User ID, Subscriber Number, key words content, retrieval date, retrieval number, purchase merchandise news and the number for buying commodity.The electric business platform commercial product recommending algorithm, pass through the purchaser record judgement of the information or other users by obtaining user, and carry out the push of dependent merchandise, it can satisfy user to push region season and much-sought-after item, and in the present invention, when not giving expression to enough interest such as information of the user to first time push, according to current season information, most several merchandise newss adjustment push content of several successfully shopping numbers of doing shopping of user location, when solving to push for the first time in time, the problem of information inaccuracy, it is effectively reduced the time of picking commodities for users, improve user experience.
Description
Technical field
The present invention relates to commercial product recommending technical field, specially a kind of electric business platform commercial product recommending algorithm.
Background technique
With the continuous development of science and technology, the quickening of social informatization process, E-commerce transaction platform constantly improve, more
The commodity needed for oneself are obtained by way of shopping online come more people, the type of commodity can be related to people day
The every aspect often lived, provides a great convenience for people's lives, due to the type of merchandize on present each electric business platform
Various, user generally requires to devote a tremendous amount of time the commodity selected and can just find oneself needs in shopping, therefore each
A businessman is each provided with the Individualized Notification Service for user, however, existing commodity push side to guarantee user experience
Method, which generally passes through, obtains user's daily browsing record or purchasing history carries out simple push, for the specific of commodity itself and
The personal information of user carries out personalized customization, so that the accuracy of pushed information is lower.
Summary of the invention
(1) the technical issues of solving
In view of the deficiencies of the prior art, the present invention provides a kind of electric business platform commercial product recommending algorithm, have, personalized customization pushes away
The advantages of sending and improve the accuracy of pushed information, solves the problems mentioned above in the background art.
(2) technical solution
To achieve the above object, the invention provides the following technical scheme: a kind of electric business platform commercial product recommending algorithm, including following step
It is rapid:
Step 1: operator's initial data is obtained:
From operator's big data system obtain user and retrieval transcript, comprising: User ID, Subscriber Number, key words content,
Retrieve date, retrieval number, purchase merchandise news and the number for buying commodity；
Step 2: intent features analysis is extracted:
The first push mark is obtained by key words content and retrieval number, obtains the commodity letter comprising the first push mark
Breath, is denoted as the first merchandise news, first merchandise news is pushed to the user；
Step 3: it obtains the first commodity push user and browses information:
Browsing time and number of the user to first merchandise news are obtained, judges the user to first commodity
Information does not express purchase intention, and between the browsing be less than preset time when, obtain current season information, believed according to current season
Breath obtains the second push mark, obtains the merchandise news comprising the second push mark, is denoted as the second merchandise news, will be described
Second merchandise news is pushed to the user；
Step 4: several several most merchandise newss of number of doing shopping successfully and do shopping of user location are obtained:
Browsing time and number of the user to second merchandise news are obtained, judges the user to second commodity
Information does not express purchase intention, and between the browsing be less than preset time when, obtain user location several do shopping successfully
Several most merchandise newss with shopping number obtain third push mark according to shopping success rate and shopping number, obtain
Merchandise news comprising third push mark, is denoted as third merchandise news, the third merchandise news is pushed to described
User；
Step 5: extracting keyword, sets weight；
Browsing time and number of the user to the third merchandise news are obtained, judges the user to the third commodity
Information does not express purchase intention, and between the browsing be less than preset time when, obtain the first merchandise news, the second merchandise news and
Browsing time longer merchandise news in third merchandise news, according to the first merchandise news, the second merchandise news and third commodity
The browsing time, longer merchandise news obtained the 4th merchandise news in information, and the 4th merchandise news is pushed to the use
Family.
Preferably, user information is read from operator database, information is analyzed, to obtain the use of the user
Family ID, Subscriber Number, key words content, retrieval date, retrieval number, purchase merchandise news and the number for buying commodity, pass through
Data acquire the information for acquiring the user in real time.
Preferably, according to the shopping information of the shopping information of the user and other users, judge beneficial to the user
The type of merchandise and whether identical to the beneficial type of merchandise of the other users, and determining the quotient beneficial to the user
Category type and it is whether identical to the beneficial type of merchandise of the other users when, Xiang Suoshu user recommends other users purchase
Commodity.
Preferably, judging unit, for the merchandise news according to the information acquired by the acquisition unit browsing time and
Number judges that the user expresses purchase intention to the merchandise news.
Preferably, acquiring unit, for obtaining browsing time and the number of the merchandise news of user.
(3) beneficial effect
Compared with prior art, the present invention provides a kind of electric business platform commercial product recommending algorithm, have it is following the utility model has the advantages that
The electric business platform commercial product recommending algorithm passes through the purchase of information and retrieval transcript or other users by obtaining user
Record judgement is bought, and carries out the push of dependent merchandise, user is can satisfy and region season and much-sought-after item is pushed, and
In the present invention, when not giving expression to enough interest such as information of the user to first time push, according to current season information, user
Location several do shopping several merchandise newss and user that successfully shopping number is most are to the third merchandise news
Browsing time number adjustment push content, and pushed information is sent to user, when solving to push for the first time in time, information inaccuracy
The problem of, it is effectively reduced the time of picking commodities for users, improves user experience.
Detailed description of the invention
Fig. 1 is a kind of electric business platform commercial product recommending algorithm flow schematic diagram proposed by the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, a kind of electric business platform commercial product recommending algorithm, comprising the following steps:
Step 1: operator's initial data is obtained:
From operator's big data system obtain user and retrieval transcript, comprising: User ID, Subscriber Number, key words content,
Retrieve date, retrieval number, purchase merchandise news and the number for buying commodity；User information is read from operator database,
Information is analyzed, to obtain User ID, the Subscriber Number, key words content, retrieval date, retrieval number, purchase of user
The number of merchandise news and purchase commodity, the information of acquisition user in real time is acquired by data.
Step 2: intent features analysis is extracted:
The first push mark is obtained by key words content and retrieval number, obtains the merchandise news comprising the first push mark,
It is denoted as the first merchandise news, the first merchandise news is pushed to user；
Step 3: it obtains the first commodity push user and browses information:
Browsing time and number of the user to the first merchandise news are obtained, judges that user does not express purchase meaning to the first merchandise news
To, and browse between be less than preset time when, obtain current season information, according to current season acquisition of information second push indicate,
The merchandise news comprising the second push mark is obtained, the second merchandise news is denoted as, the second merchandise news is pushed to user；Judgement
Unit, the browsing time of the merchandise news for being got according to acquiring unit and number judge that user expresses merchandise news
Purchase intention.
Step 4: several several most merchandise newss of number of doing shopping successfully and do shopping of user location are obtained:
Browsing time and number of the user to the second merchandise news are obtained, judges that user does not express purchase meaning to the second merchandise news
To, and browse between be less than preset time when, obtain user location several do shopping successfully and do shopping numbers at most it is several
A merchandise news obtains third push mark according to shopping success rate and shopping number, obtains the quotient comprising third push mark
Product information is denoted as third merchandise news, and third merchandise news is pushed to user；According to the shopping information and other users of user
Shopping information, judge the type of merchandise beneficial to the user and whether identical to the beneficial type of merchandise of other users, with
And determine the type of merchandise beneficial to user and to the beneficial type of merchandise of other users it is whether identical when, recommend it to user
The commodity of his user's purchase.
Step 5: extracting keyword, sets weight；
Browsing time and number of the user to third merchandise news are obtained, judges that user does not express purchase meaning to third merchandise news
To when and when being less than preset time between browsing, obtaining the first merchandise news, browsing in the second merchandise news and third merchandise news
Between longer merchandise news, it is longer according to the browsing time in the first merchandise news, the second merchandise news and third merchandise news
Merchandise news obtains the 4th merchandise news, the 4th merchandise news is pushed to user, acquiring unit, for obtaining the commodity of user
The browsing time of information and number.
In conclusion the electric business platform commercial product recommending algorithm, the electric business platform commercial product recommending algorithm, by being used by obtaining
The information and retrieval transcript or the judgement of the purchaser record of other users at family, and the push of dependent merchandise is carried out, it can satisfy
User pushes region season and much-sought-after item, and in the present invention, if user is to the non-table of information of first time push
When up to enough interest out, if most according to current season information, several successfully shopping numbers of doing shopping of user location
Dry merchandise news and user push content to the browsing time number adjustment of the third merchandise news, and send and push away to user
It delivers letters breath, when solving to push for the first time in time, the problem of information inaccuracy is effectively reduced the time of picking commodities for users,
Improve user experience.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence " including one ", it is not excluded that including
There is also other identical elements in the process, method, article or equipment of the element.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (5)
1. a kind of electric business platform commercial product recommending algorithm, it is characterised in that: the following steps are included:
Step 1: operator's initial data is obtained:
From operator's big data system obtain user and retrieval transcript, comprising: User ID, Subscriber Number, key words content,
Retrieve date, retrieval number, purchase merchandise news and the number for buying commodity；
Step 2: intent features analysis is extracted:
The first push mark is obtained by key words content and retrieval number, obtains the commodity letter comprising the first push mark
Breath, is denoted as the first merchandise news, first merchandise news is pushed to the user；
Step 3: it obtains the first commodity push user and browses information:
Browsing time and number of the user to first merchandise news are obtained, judges the user to first commodity
Information does not express purchase intention, and between the browsing be less than preset time when, obtain current season information, believed according to current season
Breath obtains the second push mark, obtains the merchandise news comprising the second push mark, is denoted as the second merchandise news, will be described
Second merchandise news is pushed to the user；
Step 4: several several most merchandise newss of number of doing shopping successfully and do shopping of user location are obtained:
Browsing time and number of the user to second merchandise news are obtained, judges the user to second commodity
Information does not express purchase intention, and between the browsing be less than preset time when, obtain user location several do shopping successfully
Several most merchandise newss with shopping number obtain third push mark according to shopping success rate and shopping number, obtain
Merchandise news comprising third push mark, is denoted as third merchandise news, the third merchandise news is pushed to described
User；
Step 5: extracting keyword, sets weight；
Browsing time and number of the user to the third merchandise news are obtained, judges the user to the third commodity
Information does not express purchase intention, and between the browsing be less than preset time when, obtain the first merchandise news, the second merchandise news and
Browsing time longer merchandise news in third merchandise news, according to the first merchandise news, the second merchandise news and third commodity
The browsing time, longer merchandise news obtained the 4th merchandise news in information, and the 4th merchandise news is pushed to the use
Family.
2. a kind of electric business platform commercial product recommending algorithm according to claim 1, it is characterised in that: from operator database
User information is read, information is analyzed, to obtain User ID, the Subscriber Number, key words content, retrieval day of the user
Phase, retrieval number, purchase merchandise news and the number for buying commodity, the information of the user is acquired by data acquisition in real time.
3. a kind of electric business platform commercial product recommending algorithm according to claim 1, it is characterised in that: according to the purchase of the user
The shopping information of object information and other users judges the type of merchandise beneficial to the user and beneficial to the other users
Whether the type of merchandise is identical, and is determining the type of merchandise beneficial to the user and the commodity beneficial to the other users
When whether type is identical, Xiang Suoshu user recommends the commodity of the other users purchase.
4. a kind of electric business platform commercial product recommending algorithm according to claim 1, it is characterised in that: judging unit is used for root
The browsing time of the merchandise news got according to the acquiring unit and number, judge the user to the merchandise news
Express purchase intention.
5. a kind of electric business platform commercial product recommending algorithm according to claim 1, it is characterised in that: acquiring unit, for obtaining
Take browsing time and the number of the merchandise news at family.
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| CN201811603331.9A CN109636545A (en) | 2018-12-26 | 2018-12-26 | A kind of electric business platform commercial product recommending algorithm |
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| CN201811603331.9A CN109636545A (en) | 2018-12-26 | 2018-12-26 | A kind of electric business platform commercial product recommending algorithm |
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Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287410A (en) * | 2019-06-05 | 2019-09-27 | 达疆网络科技(上海)有限公司 | The fusion method of a variety of proposed algorithms of user under a kind of O2O electric business scene |
| CN110415085A (en) * | 2019-07-31 | 2019-11-05 | 孙海龙 | A kind of commodity screening, methods of exhibiting and device based on geographical location information |
| CN110490707A (en) * | 2019-08-13 | 2019-11-22 | 蚌埠聚本电子商务产业园有限公司 | It is a kind of to search for purchase method for hunting for novelty for e-commerce |
| CN110516051A (en) * | 2019-07-26 | 2019-11-29 | 北京搜狗科技发展有限公司 | A kind of data processing method, device and electronic equipment |
| CN110704744A (en) * | 2019-09-30 | 2020-01-17 | 北京金山安全软件有限公司 | Method and device for recommending target object to user and electronic equipment |
| CN110807669A (en) * | 2019-10-31 | 2020-02-18 | 深圳市云积分科技有限公司 | Cross-platform user information management method and device |
| CN110807667A (en) * | 2019-10-31 | 2020-02-18 | 深圳市云积分科技有限公司 | Method and device for activating sleeping customers |
| CN110827114A (en) * | 2019-10-01 | 2020-02-21 | 榕知科技(武汉)有限公司 | Commodity recommendation method and device |
| CN110855765A (en) * | 2019-11-05 | 2020-02-28 | 北京十分科技有限公司 | Pushing method, pushing device, issuing method and issuing device for promotion gift package |
| CN110956027A (en) * | 2019-12-03 | 2020-04-03 | 深圳市云积分科技有限公司 | Method and device for generating digital short message content |
| CN111127164A (en) * | 2019-12-26 | 2020-05-08 | 韶关学院 | Novel recommendation algorithm |
| CN111192112A (en) * | 2019-12-30 | 2020-05-22 | 深圳市云积分科技有限公司 | Multi-platform interaction method and device |
| CN111984837A (en) * | 2019-05-23 | 2020-11-24 | 浙江口碑网络技术有限公司 | Commodity data processing method, device and equipment |
| CN112100494A (en) * | 2020-09-14 | 2020-12-18 | 青岛黄海学院 | Database for electronic commerce and working method |
| CN112102037A (en) * | 2020-09-16 | 2020-12-18 | 汤涛 | Live E-commerce platform commodity content intelligent pushing management system based on big data |
| CN112581236A (en) * | 2020-12-28 | 2021-03-30 | 北京滴普科技有限公司 | Intelligent commodity recommending method and system and readable storage medium thereof |
| CN112738536A (en) * | 2020-12-24 | 2021-04-30 | 贵州国创唯品科技有限公司 | Data matching method based on social live broadcast e-commerce distribution |
| CN112950256A (en) * | 2021-02-02 | 2021-06-11 | 广东便捷神科技股份有限公司 | Method and system for pushing customized advertisement form based on App |
| CN115456721A (en) * | 2022-09-16 | 2022-12-09 | 广州森岛科技有限公司 | Internet-based electronic commerce commodity pushing method and system |
| CN116109338A (en) * | 2022-12-12 | 2023-05-12 | 广东南粤分享汇控股有限公司 | Electric business analysis method and system based on artificial intelligence |
| CN116611897A (en) * | 2023-07-19 | 2023-08-18 | 宜宾叙控科技有限公司 | Message reminding method and system based on artificial intelligence |
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| CN111984837A (en) * | 2019-05-23 | 2020-11-24 | 浙江口碑网络技术有限公司 | Commodity data processing method, device and equipment |
| CN111984837B (en) * | 2019-05-23 | 2021-04-23 | 浙江口碑网络技术有限公司 | Commodity data processing method, device and equipment |
| CN110287410A (en) * | 2019-06-05 | 2019-09-27 | 达疆网络科技(上海)有限公司 | The fusion method of a variety of proposed algorithms of user under a kind of O2O electric business scene |
| CN110516051A (en) * | 2019-07-26 | 2019-11-29 | 北京搜狗科技发展有限公司 | A kind of data processing method, device and electronic equipment |
| CN110415085A (en) * | 2019-07-31 | 2019-11-05 | 孙海龙 | A kind of commodity screening, methods of exhibiting and device based on geographical location information |
| CN110490707A (en) * | 2019-08-13 | 2019-11-22 | 蚌埠聚本电子商务产业园有限公司 | It is a kind of to search for purchase method for hunting for novelty for e-commerce |
| CN110704744A (en) * | 2019-09-30 | 2020-01-17 | 北京金山安全软件有限公司 | Method and device for recommending target object to user and electronic equipment |
| CN110827114A (en) * | 2019-10-01 | 2020-02-21 | 榕知科技(武汉)有限公司 | Commodity recommendation method and device |
| CN110807667A (en) * | 2019-10-31 | 2020-02-18 | 深圳市云积分科技有限公司 | Method and device for activating sleeping customers |
| CN110807669A (en) * | 2019-10-31 | 2020-02-18 | 深圳市云积分科技有限公司 | Cross-platform user information management method and device |
| CN110807667B (en) * | 2019-10-31 | 2023-09-22 | 深圳市云积分科技有限公司 | Method and device for activating sleeping clients |
| CN110855765A (en) * | 2019-11-05 | 2020-02-28 | 北京十分科技有限公司 | Pushing method, pushing device, issuing method and issuing device for promotion gift package |
| CN110956027A (en) * | 2019-12-03 | 2020-04-03 | 深圳市云积分科技有限公司 | Method and device for generating digital short message content |
| CN111127164A (en) * | 2019-12-26 | 2020-05-08 | 韶关学院 | Novel recommendation algorithm |
| CN111192112A (en) * | 2019-12-30 | 2020-05-22 | 深圳市云积分科技有限公司 | Multi-platform interaction method and device |
| CN112100494A (en) * | 2020-09-14 | 2020-12-18 | 青岛黄海学院 | Database for electronic commerce and working method |
| CN112102037A (en) * | 2020-09-16 | 2020-12-18 | 汤涛 | Live E-commerce platform commodity content intelligent pushing management system based on big data |
| CN112102037B (en) * | 2020-09-16 | 2021-05-07 | 广州伊的家网络科技有限公司 | Live E-commerce platform commodity content intelligent pushing management system based on big data |
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| CN112581236A (en) * | 2020-12-28 | 2021-03-30 | 北京滴普科技有限公司 | Intelligent commodity recommending method and system and readable storage medium thereof |
| CN112950256A (en) * | 2021-02-02 | 2021-06-11 | 广东便捷神科技股份有限公司 | Method and system for pushing customized advertisement form based on App |
| CN112950256B (en) * | 2021-02-02 | 2023-05-23 | 广东便捷神科技股份有限公司 | Method and system for customizing advertisement form based on App pushing |
| CN115456721A (en) * | 2022-09-16 | 2022-12-09 | 广州森岛科技有限公司 | Internet-based electronic commerce commodity pushing method and system |
| CN115456721B (en) * | 2022-09-16 | 2023-12-12 | 广东朝阳全网通科技有限公司 | Internet-based electronic commerce commodity pushing method and system |
| CN116109338A (en) * | 2022-12-12 | 2023-05-12 | 广东南粤分享汇控股有限公司 | Electric business analysis method and system based on artificial intelligence |
| CN116109338B (en) * | 2022-12-12 | 2023-11-24 | 广东南粤分享汇控股有限公司 | Electric business analysis method and system based on artificial intelligence |
| CN116611897A (en) * | 2023-07-19 | 2023-08-18 | 宜宾叙控科技有限公司 | Message reminding method and system based on artificial intelligence |
| CN116611897B (en) * | 2023-07-19 | 2023-10-13 | 北京快益通科技有限公司 | Message reminding method and system based on artificial intelligence |
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Application publication date: 20190416 |