Multi-Tower Multi-Interest Recommendation with User Representation Repel

arXiv:2403.05122v1 Announce Type: cross
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, …

abstract academia arxiv attention cs.ir cs.lg industry information multiple overload recommendation recommender systems representation systems type value