Multi-Tower Multi-Interest Recommendation with User Representation Repel
<|reference_start|>Multi-Tower Multi-Interest Recommendation with User Representation Repel: 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.<|reference_end|>
@article{xiong2024multi-tower,
    title={Multi-Tower Multi-Interest Recommendation with User Representation Repel},
    author={Tianyu Xiong, Xiaohan Yu},
    journal={arXiv preprint arXiv:2403.05122},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05122},
    primaryClass={cs.IR cs.LG}
}