Multi-Tower Multi-Interest Recommendation with User Representation Repel
- URL: http://arxiv.org/abs/2403.05122v1
- Date: Fri, 8 Mar 2024 07:36:14 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: 1.1657633779338725
- 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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