Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866917614343684096 |
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| author | Liu, Qijiong Fan, Lu Xiao, Jiaren Zhu, Jieming Wu, Xiao-Ming |
| author_facet | Liu, Qijiong Fan, Lu Xiao, Jiaren Zhu, Jieming Wu, Xiao-Ming |
| contents | Category information plays a crucial role in enhancing the quality and personalization of recommender systems. Nevertheless, the availability of item category information is not consistently present, particularly in the context of ID-based recommendations. In this work, we propose a novel approach to automatically learn and generate entity (i.e., user or item) category trees for ID-based recommendation. Specifically, we devise a differentiable vector quantization framework for automatic category tree generation, namely CAGE, which enables the simultaneous learning and refinement of categorical code representations and entity embeddings in an end-to-end manner, starting from the randomly initialized states. With its high adaptability, CAGE can be easily integrated into both sequential and non-sequential recommender systems. We validate the effectiveness of CAGE on various recommendation tasks including list completion, collaborative filtering, and click-through rate prediction, across different recommendation models. We release the code and data for others to reproduce the reported results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_16761 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization Liu, Qijiong Fan, Lu Xiao, Jiaren Zhu, Jieming Wu, Xiao-Ming Information Retrieval Category information plays a crucial role in enhancing the quality and personalization of recommender systems. Nevertheless, the availability of item category information is not consistently present, particularly in the context of ID-based recommendations. In this work, we propose a novel approach to automatically learn and generate entity (i.e., user or item) category trees for ID-based recommendation. Specifically, we devise a differentiable vector quantization framework for automatic category tree generation, namely CAGE, which enables the simultaneous learning and refinement of categorical code representations and entity embeddings in an end-to-end manner, starting from the randomly initialized states. With its high adaptability, CAGE can be easily integrated into both sequential and non-sequential recommender systems. We validate the effectiveness of CAGE on various recommendation tasks including list completion, collaborative filtering, and click-through rate prediction, across different recommendation models. We release the code and data for others to reproduce the reported results. |
| title | Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2308.16761 |