E-commerce personalized recommendation method based on cross-domain collaborative filtering

Through the combination of cross-domain collaborative filtering and knowledge graphs, the data of the e-commerce platform is integrated, data heterogeneity and cold start problems are solved, more accurate and personalized recommendations are achieved, and user experience and platform activity are improved.

CN120430856AInactive Publication Date: 2025-08-05YUNNAN HUAWU TECHNOLOGY CO LTD
View PDF 0 Cites 7 Cited by

Patent Information

Application Number
CN202510579045.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-07
Publication Date
2025-08-05
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Data heterogeneity among existing e-commerce platforms leads to inaccurate recommendations, and there are cold start problems for new users or new products, affecting the user experience.

Method used

Cross-domain collaborative filtering method is adopted to integrate user behavior data and product attribute data in different fields, build user portraits and product portraits, use knowledge graphs to transfer interests and recommend them, and combine content and popular product recommendations to optimize recommendation results.

Benefits of technology

It improves the accuracy of recommendations and user satisfaction, alleviates the cold start problem of new users and new products, and enhances user stickiness and platform competitiveness.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN120430856A_ABST
    Figure CN120430856A_ABST
Patent Text Reader

Abstract

The invention discloses an e-commerce personalized recommendation method based on cross-domain collaborative filtering, and relates to the technical field of e-commerce, and the method comprises the steps: collecting and preprocessing data, including user behavior data and commodity attribute data, of different fields of an e-commerce platform; materializing the collected data in different fields, and integrating the data into a structured e-commerce knowledge graph; and constructing a user-based cross-domain collaborative filtering model based on the behavior data of the user in the multiple fields, and predicting commodities in which the user is interested. By integrating data in different fields and constructing user portraits and commodity portraits, the problem of inaccurate recommendation caused by data heterogeneity in a traditional recommendation system is effectively solved, interests and preferences of users in different fields can be more accurately captured through cross-domain data association analysis, and the recommendation efficiency is improved. Therefore, personalized recommendation better meeting the requirements of the user is provided for the user, the recommendation accuracy and satisfaction degree are remarkably improved, and the user stickiness and the platform competitiveness are enhanced.
Need to check novelty before this filing date? Find Prior Art

Citation Information

Cited By

  • Accurate advertisement pushing method and system based on take-out platform user data

    CN120807061A

  • Commodity collaborative filtering recommendation method and system based on user behavior analysis

    CN120929682A

  • Semantic analysis-based user portrait recognition method and website recommendation method

    CN120950779A

  • Data retrieval method based on multivariate matrix fusion

    CN121117280A

  • A data retrieval method based on multi-element matrix fusion

    CN121117280B