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Co-evolving Vector Quantization for ID-based Recommendation
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
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