Multi-Tower Multi-Interest Recommendation with User Representation Repel
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910547711098880 |
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| author | Xiong, Tianyu Yu, Xiaohan |
| author_facet | Xiong, Tianyu Yu, Xiaohan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Tower Multi-Interest Recommendation with User Representation Repel Xiong, Tianyu Yu, Xiaohan Information Retrieval Machine Learning H.3.3 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. |
| title | Multi-Tower Multi-Interest Recommendation with User Representation Repel |
| topic | Information Retrieval Machine Learning H.3.3 |
| url | https://arxiv.org/abs/2403.05122 |