Multi-Tower Multi-Interest Recommendation with User Representation Repel

Understand

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.