Correlation analysis algorithm based commodity recommendation method

A technology for product recommendation and correlation analysis, applied in business, computing, instruments, etc., can solve problems such as cold start, original matrix failure, and rapid user increase, and achieve the effect of small calculation amount and improved correlation degree.

Inactive Publication Date: 2017-10-20
SHANGHAI XUWEI INTERNET OF THINGS TECH
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Problems solved by technology

[0003] The user-based collaborative filtering algorithm takes each user as the subject of analysis, and for each group, calculates the degree of association between each user in the group and other users in the group by processing the product browsing logs of each user in the group , and then recommend to the user the products that he has not browsed or purchased but is purchased or browsed by other users with high correlation in the group; the disadvantage of this algo...

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  • Correlation analysis algorithm based commodity recommendation method

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Embodiment

[0038] Such as figure 1 As shown, a product recommendation method based on association analysis algorithm is used to recommend products based on user behavior logs in shopping websites. The method includes the following steps:

[0039] S1, obtain historical user behavior logs, perform fuzzy clustering analysis on users and products according to the data in the historical user behavior logs, generate multiple user groups and product categories, and the evaluation scores of each user group for each product category;

[0040] In this step, each user group has a characteristic, such as age group, region, shopping preference, and the region is determined according to the information filled in when the user registers or the location of the user's registered mobile phone number or the user's delivery address; each user is classified into at least one user Each user group overlaps with users in another user group, that is, each user has at least one feature at the same time. For exam...

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Abstract

The present invention relates to a commodity recommendation method based on an association analysis algorithm. Commodity recommendation is performed according to user behavior logs in shopping websites. Carry out fuzzy clustering analysis with products to generate multiple user groups and product categories, and each user group’s evaluation score for each product category; S2, build recommendation matrix M; S3, obtain new user behavior logs, use recommendation matrix M recommends products to users in the newly added user behavior log; S4, judges whether the data volume of the newly added user behavior log has reached the set value, if so, returns to step S1, and counts the data volume of the newly added user behavior log value, if not, return to step S3. Compared with the prior art, the present invention has the advantages of small amount of calculation, high reliability and strong pertinence, and has adaptability to constantly changing market consumption trends.

Description

technical field [0001] The invention relates to a method for recommending online commodities, in particular to a method for recommending commodities based on an association analysis algorithm. Background technique [0002] Product recommendation algorithms used by shopping websites usually include: [0003] The user-based collaborative filtering algorithm takes each user as the subject of analysis, and for each group, calculates the degree of association between each user of the group and other users of the group by processing the product browsing logs of each user in the group , and then recommend to the user the products that he has not browsed or purchased but is purchased or browsed by other users with high correlation in the group; the disadvantage of this algorithm is that the user increases rapidly, but only the user's historical preference data is used to calculate the user similarity degree, there is a "cold start" problem for new users. [0004] The model-based c...

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Application Information

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IPC IPC(8): G06Q30/06
CPCG06Q30/0631
Inventor 姚薇俞祥祥
Owner SHANGHAI XUWEI INTERNET OF THINGS TECH
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