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.

翻译：在信息过载的时代，推荐系统的价值已在学术界和工业界得到广泛认可。特别是多兴趣序列推荐，作为近年来备受关注的子领域，通过生成多个用户表示，多兴趣学习模型在理论和实践上均表现出比单用户表示模型更优的表达能力。尽管该领域取得了重大进展，但三个主要问题仍持续困扰多兴趣学习方法的性能与可采纳性：训练与部署目标的差异、无法获取物品信息、以及因单塔架构导致的工业落地困难。针对这些挑战，我们提出了一种新颖的带用户表示排斥的多塔多兴趣推荐框架。跨多个大规模工业数据集的实验结果证明了该框架的有效性和泛化能力。