Multi-Tower Multi-Interest Recommendation with User Representation Repel
Tianyu Xiong Xiaohan Yu
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.