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- W4386384772 abstract "Category information plays a crucial role in enhancing the quality and personalization of recommendations. Nevertheless, the availability of item category information is not consistently present, particularly in the context of ID-based recommendations. In this work, we propose an alternative approach to automatically learn and generate entity (i.e., user and item) categorical information at different levels of granularity, specifically for ID-based recommendation. Specifically, we devise a co-evolving vector quantization framework, namely COVE, which enables the simultaneous learning and refinement of code representation and entity embedding in an end-to-end manner, starting from the randomly initialized states. With its high adaptability, COVE can be easily integrated into existing recommendation models. We validate the effectiveness of COVE on various recommendation tasks including list completion, collaborative filtering, and click-through rate prediction, across different recommendation models. We will publish the code and data for other researchers to reproduce our work." @default.
- W4386384772 created "2023-09-02" @default.
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- W4386384772 date "2023-08-31" @default.
- W4386384772 modified "2023-09-30" @default.
- W4386384772 title "Co-evolving Vector Quantization for ID-based Recommendation" @default.
- W4386384772 doi "https://doi.org/10.48550/arxiv.2308.16761" @default.
- W4386384772 hasPublicationYear "2023" @default.
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