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.

在信息过载的时代，推荐系统的价值在学术界和工业界都被广泛认可。尤其是多兴趣顺序推荐，是一个近年来受到越来越多关注的子领域。通过生成多个用户表示，多兴趣学习模型在理论和实证上都展示了比单个用户表示模型更出色的表现。尽管该领域有了重大进展，但仍然存在三个主要问题困扰着多兴趣学习方法的性能和可采用性，即训练和部署目标之间的差异，无法访问项目信息以及由于其单塔架构所导致的工业应用难度。我们通过提出一种具有用户表示排斥的新型多塔多兴趣框架来解决这些挑战。多个大规模工业数据集上的实验结果证明了我们提出的框架的有效性和泛化能力。