Multi-Tower Multi-Interest Recommendation with User Representation Repel

Autor:	Xiong, Tianyu, Yu, Xiaohan
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning H.3.3
Druh dokumentu:	Working Paper
Popis:	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. Comment: Not accepted by conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.05122 Zobrazit plný text záznamu View this record from Arxiv