A user data sharing method for a furniture platform and related devices

By calculating the synergy between the feature vectors of users and furniture in the furniture platform, a hot-start furniture decision domain is established, and furniture recommendation information is adjusted. This solves the problem of insufficient recommendations for potential furniture orders in existing technologies and achieves more accurate furniture recommendations.

CN120198200BActive Publication Date: 2026-07-03SHENZHEN HESHECASA TECH CO LTD
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Patent Information

Application Number
CN202510316396.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-03-18
Publication Date
2026-07-03
Estimated Expiration
2045-03-18

AI Technical Summary

Technical Problem

In existing technologies, furniture platforms fail to effectively utilize user data from target users who lack order and browsing history when recommending furniture, resulting in insufficient recommendations for potential furniture orders.

Method used

By acquiring shared data from furniture platforms, we can determine the feature vectors of furniture and users, calculate the degree of synergy between users, establish a hot-start furniture decision domain, and adjust furniture recommendation information based on the degree of synergy and the set of convergence values ​​to achieve personalized recommendations for potential furniture orders from target users.

Benefits of technology

It improved the accuracy of furniture recommendations, increased the proportion of furniture recommended to potential customers, and enabled personalized furniture recommendations for target users.

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Abstract

The application provides a user data sharing method for a furniture platform and related equipment. The method comprises the following steps: obtaining shared data in a specified furniture platform, determining furniture feature vectors of each furniture and user feature vectors of each user in the shared data, determining a set of approaching values of each user feature vector according to all the furniture feature vectors, determining a degree of cooperation between two users according to each two sets of approaching values, determining a hot start furniture decision domain of a target user according to all the degrees of cooperation, determining shared prediction values of each furniture in the hot start furniture decision domain, and then determining adjustment coefficients of the target user for each furniture, adjusting furniture recommendation information of the target user according to the adjustment coefficients of the target user for each furniture, and sharing the adjusted furniture recommendation information to a shared database of the furniture platform, so that personalized sharing recommendation of potential ordered furniture of the target user can be realized.
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