Commodity recommendation method based on fusion of multiple user representations

By employing adversarial fusion networks and multi-granularity matching mechanisms, the problems of distributional differences and semantic inconsistencies in multi-source user representations are resolved. This enables the understanding of complex relationships between users and items, improves the accuracy and diversity of the recommendation system, and provides richer personalized recommendations.

CN122022952APending Publication Date: 2026-05-12TIANJIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202610142524.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-02
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

In existing technologies, recommendation methods based on multi-source user representations ignore the distributional differences and semantic gaps between user representations from different sources, leading to feature conflicts and semantic confusion. This makes it difficult to maintain recommendation accuracy in scenarios with sparse data or missing information. Furthermore, traditional methods struggle to capture the multi-granular relationships between users and items, limiting the performance improvement of recommendation systems.

Method used

An adversarial fusion network architecture is adopted, which calculates the relevance weights between user representations through a cross-view attention mechanism and uses a view discriminator to identify residual features. The gradient inversion layer is combined to optimize representation fusion. A multi-granularity matching mechanism is used to calculate the matching degree between users and items, and diversity constraints are introduced to generate a recommendation list.

Benefits of technology

It achieves semantic alignment of user representations, improves the quality and discriminative ability of fused representations, enhances the accuracy and diversity of recommendations, avoids the information cocoon effect, and provides a richer user experience.

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Abstract

The invention relates to the technical field of recommendation systems, in particular to a commodity recommendation method based on fusion of multiple user representations. According to the method, firstly, various heterogeneous user representations such as behavior representations, content representations and social representations of users are obtained, then representation alignment is carried out through an adversarial fusion network, and a multi-view fusion device generates a unified fusion representation by using a cross-view attention mechanism; the view discriminator discriminates view specific features through a three-layer neural network and achieves antagonism training through a gradient inversion layer. And based on the aligned fusion representation, a multi-granularity matching mechanism is adopted to calculate the matching degree of the user and the article, and the representation is projected to three subspaces to calculate the similarity and perform weighted fusion. And finally, introducing diversity constraints when a recommendation list is generated, carrying out weight reduction processing on articles of the same category, and controlling category distribution. According to the method, the problem of distribution difference during multi-source user representation fusion is effectively solved, and the recommendation accuracy and diversity are improved.
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