Multi-Tower Multi-Interest Recommendation with User Representation Repel

• URL: http://arxiv.org/abs/2403.05122v2
• Date: Wed, 31 Jul 2024 04:58:56 GMT
• Title: Multi-Tower Multi-Interest Recommendation with User Representation Repel
• Authors: Tianyu Xiong, Xiaohan Yu,
• Abstract summary: We propose a novel multi-tower multi-interest framework with user representation repel. Experimental results across multiple large-scale industrial datasets proved the effectiveness and generalizability of our proposed framework.
• Score: 0.9867914513513453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the era of information overload, the value of recommender systems has been profoundly recognized in academia and industry alike. Multi-interest sequential recommendation, in particular, is a subfield that has been receiving increasing attention in recent years. By generating multiple-user representations, multi-interest learning models demonstrate superior expressiveness than single-user representation models, both theoretically and empirically. Despite major advancements in the field, three major issues continue to plague the performance and adoptability of multi-interest learning methods, the difference between training and deployment objectives, the inability to access item information, and the difficulty of industrial adoption due to its single-tower architecture. We address these challenges by proposing a novel multi-tower multi-interest framework with user representation repel. Experimental results across multiple large-scale industrial datasets proved the effectiveness and generalizability of our proposed framework.

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