Multi Interests for Joint Search-Recommendation Modeling

• URL: http://arxiv.org/abs/2608.10535v1
• Date: Tue, 11 Aug 2026 06:15:43 GMT
• Title: Multi Interests for Joint Search-Recommendation Modeling
• Abstract summary: We propose a multi-interest-based mixed sequential modeling framework MIJSR.<n>Our model can be roughly divided into three modules: cross-domain behavior fusion, multi-interest mining, and multi-task prediction.<n>Our model can further enhance its accuracy in search and recommendation by extracting users' multi interests at a fine-grained level.
• Score: 6.74797033837872
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Search and recommendation are crucial for understanding user preferences. More and more studies are attempting to jointly model search behavior and recommendation behavior, by integrating user active search and passive recommendation behavior data to better mine user preferences. However, although existing cross-domain unified modeling frameworks can effectively compensate for the differences in behavior between domains, they overlook the expression of interests in different scenarios under mixed sequences. In this study, we propose a multi-interest-based mixed sequential modeling framework MIJSR, which performs multi-interest mining and adaptive integration on search recommendation mixed sequences from both structural and semantic perspectives. Specifically, our model can be roughly divided into three modules: cross-domain behavior fusion, multi-interest mining, and multi-task prediction. Firstly, we align the representations of query and item through contrastive learning training. Then, we extract the multi interests of the mixed behavior sequence from both structural and semantic perspectives. Structurally, we extract search interests, recommendation interests, and cross interests through subsequence partitioning and mask settings; In terms of semantics, we use the semantic information of queries for clustering and perform semantic segmentation on mixed sequences to construct semantic multi interests. Finally, the adaptive fusion of multiple interests is combined with other side information to use a progressive layered extraction model for multi-task prediction. Extensive experiments on two open-source datasets have shown that our model can further enhance its accuracy in search and recommendation by extracting users' multi interests at a fine-grained level. Codes are available at https://github.com/pxcstart/MIJSR.

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