CN108734553B - Commodity recommendation method and system - Google Patents
Commodity recommendation method and system Download PDFInfo
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- CN108734553B CN108734553B CN201810461595.9A CN201810461595A CN108734553B CN 108734553 B CN108734553 B CN 108734553B CN 201810461595 A CN201810461595 A CN 201810461595A CN 108734553 B CN108734553 B CN 108734553B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0282—Rating or review of business operators or products
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Abstract
The invention provides a commodity recommendation method and a commodity recommendation system, and relates to the field of electronic commerce. The commodity recommendation method and the system of the invention comprise the steps of obtaining all commodity information and total commodity quantity purchased by a user; acquiring all evaluations of a user; judging whether the evaluation of the user on each commodity is the user evaluation in person; extracting the number of all self-evaluations of the user; and comparing the number of the personal evaluations with the total number of the commodities to obtain a comparison result, and recommending the commodities to the user according to the comparison result. By adopting the pushing method, the commodities are pushed to the user according to the evaluation of the user, so that targeted pushing is realized, and the user can find the required commodities more quickly.
Description
Technical Field
The invention relates to the field of electronic commerce, in particular to a commodity recommendation method and a commodity recommendation system.
Background
With the development of information technology and the internet, people gradually move from an information-poor era to an information-overloaded era. In this age, both information consumers and information producers have met with significant challenges. For information consumers, it is very difficult to find information of interest to the consumers from a large amount of information; for information producers, it is very difficult to make the information produced by the producers stand out and receive the attention of the vast users. The push system is an important tool for solving the contradiction. The push system can contact users and information, on one hand, help users find valuable information of themselves, on the other hand, enable the information to be shown in front of users interested in it, thus realize the win-win situation of information consumer and information producer.
In the prior art, most of pushing systems carry out group pushing on users invariably, and the requirements of each user cannot be grasped.
Disclosure of Invention
The invention aims to provide a commodity recommendation method and a commodity recommendation system, and aims to solve the problem that a push system in the prior art cannot push articles according to personal preferences of customers, so that users lose confidence.
In one aspect, the present invention provides a method for recommending a commodity, including:
acquiring information of all commodities purchased by a user and the total quantity of the commodities;
acquiring all evaluations of the user;
judging whether the evaluation of the user on each commodity is the user evaluation in person;
extracting the number of all self-evaluations of the user;
comparing the number of the personal evaluations with the total number of the commodities to obtain a comparison result, and recommending the commodities to the user according to the comparison result.
Optionally, if the comparison result is greater than a preset threshold, it is determined that the user is an active user, information of the commodities purchased and evaluated by the active user is classified, a purchase type of the active user is determined, a commodity type associated with the purchase type is obtained, and the commodity type is pushed to the user.
Optionally, if the comparison result is smaller than a preset threshold, it is determined that the user is an inactive user, information of commodities purchased by the inactive user is classified, a purchase type of the active user is determined, a commodity type associated with the purchase type is obtained, and the commodity type is pushed to the user.
Optionally, the evaluation is divided into evaluation of satisfaction with the commodity and recommendation of the commodity.
Optionally, all the evaluation information of a user is classified according to the satisfaction degree, and is classified into a general evaluation, a medium evaluation and a high evaluation, wherein the general evaluation is that the user considers the commodity to be general, the medium evaluation is that the user considers the commodity to be relatively satisfied, and the high evaluation is that the user considers the commodity to be very satisfied.
Optionally, the goods are classified according to the suggestion, and the goods are recommended to the user according to the classification.
Optionally, the commodities are classified according to the suggestion, commodity information is searched according to the suggestion, and commodities meeting the user requirement are selected and recommended to the user.
Optionally, after recommending the recommended goods to the user, sending an invitation to enable the customer to evaluate the goods.
In another aspect, the present invention further provides a commodity recommendation system, including:
an acquisition unit that acquires information on all commodities purchased by a user, a total number of commodities, and all evaluations of the user;
a determination unit that determines whether the evaluation of each commodity by the user is the user's own evaluation;
the comparison unit is used for extracting the number of all the self-evaluations of the user; comparing the number of the personal evaluations with the total number of the commodities;
and the recommending unit recommends commodities to the user according to the result of the comparing unit.
The commodity recommendation method and the system of the invention comprise the steps of obtaining all commodity information and total commodity quantity purchased by a user; acquiring all evaluations of a user; judging whether the evaluation of the user on each commodity is the user evaluation in person; extracting the number of all self-evaluations of the user; and comparing the number of the personal evaluations with the total number of the commodities to obtain a comparison result, and recommending the commodities to the user according to the comparison result. By adopting the pushing method, the commodities are pushed to the user according to the evaluation of the user, so that targeted pushing is realized, and the user can find the required commodities more quickly.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 is a schematic flow chart diagram of a merchandise recommendation method according to one embodiment of the present invention;
FIG. 2 is a schematic system diagram of an item recommendation system according to one embodiment of the present invention.
Detailed Description
Fig. 1 is a schematic flowchart of an article recommendation method according to an embodiment of the present invention. As shown in fig. 1, the commodity recommendation method includes:
acquiring information of all commodities purchased by a user and the total quantity of the commodities;
acquiring all evaluations of a user;
judging whether the evaluation of the user on each commodity is the user evaluation in person;
extracting the number of all self-evaluations of the user;
and comparing the number of the personal evaluations with the total number of the commodities to obtain a comparison result, and recommending the commodities to the user according to the comparison result.
By adopting the pushing method, the commodities are pushed to the user according to the evaluation of the user, so that targeted pushing is realized, and the user can find the required commodities more quickly.
In a further embodiment of the present invention, if the comparison result is greater than the preset threshold, for example, 60%, or may be any value between 06% and 1.0, it is determined that the user is an active user, the information of the goods purchased and evaluated by the active user is classified, the purchase type of the active user is determined, the kind of the goods associated with the purchase type is obtained, and the kind of the goods is pushed to the user.
In a further embodiment of the present invention, if the comparison result is smaller than the preset threshold, it is determined that the user is an inactive user, information of commodities purchased by the inactive user is classified, a purchase type of the active user is determined, a commodity type associated with the purchase type is obtained, and the commodity type is pushed to the user.
In a further embodiment of the invention, the evaluation is divided into evaluation of the satisfaction of the goods and recommendation of the goods.
In a further embodiment of the invention, all the evaluation information of a user is classified according to the satisfaction degree, and is divided into a normal evaluation, a medium evaluation and a high evaluation, wherein the normal evaluation is that the user considers that the commodity is general, the medium evaluation is that the user considers that the commodity is relatively satisfied, and the high evaluation is that the user considers that the commodity is very satisfied.
In a further embodiment of the invention, the goods are classified according to the recommendation, and the goods are recommended to the user according to the classification.
In a further embodiment of the invention, the commodities are classified according to the suggestions, commodity information is searched according to the suggestions, and commodities meeting the requirements of the user are selected and recommended to the user.
In a further embodiment of the invention, after the recommended goods are recommended to the user, an invitation is sent to make the customer evaluate the goods.
FIG. 2 is a schematic system diagram of an item recommendation system according to one embodiment of the present invention. As shown in fig. 2, the commodity recommendation system includes an obtaining unit 1, a determining unit 2, a comparing unit 3, and a recommending unit 4.
The acquisition unit 1 acquires information on all commodities purchased by one user, the total quantity of commodities, and all evaluations of the user. The determination unit 2 is configured to determine whether the evaluation of each product by the user is a user-owned evaluation. The comparison unit 3 is used for extracting the number of all the self-evaluations of the user; comparing the number of the personal evaluation with the total number of the commodities. And the recommending unit 4 is used for recommending commodities to the user according to the result of the comparing unit.
The commodity recommendation method and the system of the invention comprise the steps of obtaining all commodity information and total commodity quantity purchased by a user; acquiring all evaluations of a user; judging whether the evaluation of the user on each commodity is the user evaluation in person; extracting the number of all self-evaluations of the user; and comparing the number of the personal evaluations with the total number of the commodities to obtain a comparison result, and recommending the commodities to the user according to the comparison result. By adopting the pushing method, the commodities are pushed to the user according to the evaluation of the user, so that targeted pushing is realized, and the user can find the required commodities more quickly.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (1)
1. A method for recommending an article, comprising:
acquiring information of all commodities purchased by a user and the total quantity of the commodities;
acquiring all evaluations of the user;
judging whether the evaluation of the user on each commodity is the user evaluation in person;
extracting the number of all self-evaluations of the user;
comparing the number of the personal evaluations with the total number of the commodities to obtain a comparison result, and recommending the commodities to the user according to the comparison result;
if the comparison result is larger than a preset threshold value, the user is judged to be an active user, the commodity information purchased and evaluated by the active user is classified, the purchase type of the active user is determined, the commodity type associated with the purchase type is obtained, and the commodity type is pushed to the user;
if the comparison result is smaller than a preset threshold value, the user is judged to be an inactive user, the information of the commodities purchased by the inactive user is classified, the purchase type of the inactive user is determined, the commodity type associated with the purchase type is obtained, and the commodity type is pushed to the user;
the evaluation comprises the steps of evaluating the satisfaction degree of the commodity and proposing a suggestion to the commodity;
classifying all evaluation information of a user according to the satisfaction degree, and classifying the evaluation information into a common evaluation, a medium evaluation and a high evaluation, wherein the common evaluation is that the user considers the commodity to be common, the medium evaluation is that the user considers the commodity to be relatively satisfied, and the high evaluation is that the user considers the commodity to be very satisfied;
classifying the commodities according to the suggestion, and recommending the commodities to the user according to the classification;
classifying the commodities according to the suggestion, searching commodity information according to the suggestion, and selecting the commodities meeting the user requirement to recommend to the user;
and sending an invitation to enable the customer to evaluate the recommended commodities after the recommended commodities are recommended to the user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810461595.9A CN108734553B (en) | 2018-05-15 | 2018-05-15 | Commodity recommendation method and system |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810461595.9A CN108734553B (en) | 2018-05-15 | 2018-05-15 | Commodity recommendation method and system |
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| CN108734553A CN108734553A (en) | 2018-11-02 |
| CN108734553B true CN108734553B (en) | 2019-12-27 |
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| CN201810461595.9A Expired - Fee Related CN108734553B (en) | 2018-05-15 | 2018-05-15 | Commodity recommendation method and system |
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Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109636530B (en) * | 2018-12-14 | 2021-02-09 | 拉扎斯网络科技（上海）有限公司 | Product determination method, product determination device, electronic equipment and computer-readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101206751A (en) * | 2007-12-25 | 2008-06-25 | 北京科文书业信息技术有限公司 | Customer recommendation system based on data digging and method thereof |
| CN105894357A (en) * | 2016-03-30 | 2016-08-24 | 北京金山安全软件有限公司 | Commodity information pushing method and device |
| CN107016589A (en) * | 2016-08-10 | 2017-08-04 | 阿里巴巴集团控股有限公司 | The determination method and device of recommended products |
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2018
- 2018-05-15 CN CN201810461595.9A patent/CN108734553B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101206751A (en) * | 2007-12-25 | 2008-06-25 | 北京科文书业信息技术有限公司 | Customer recommendation system based on data digging and method thereof |
| CN105894357A (en) * | 2016-03-30 | 2016-08-24 | 北京金山安全软件有限公司 | Commodity information pushing method and device |
| CN107016589A (en) * | 2016-08-10 | 2017-08-04 | 阿里巴巴集团控股有限公司 | The determination method and device of recommended products |
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| CN108734553A (en) | 2018-11-02 |
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