The paper presents Ada-Retrieval, a new multi-round retrieval paradigm for recommender systems. Unlike traditional single-round models, Ada-Retrieval iteratively refines user representations to better identify potential item candidates, effectively capturing the…
Continue readingThe paper introduces LLMRS, a recommender system based on Large Language Models (LLM), aimed at improving software purchase suggestions. Traditional recommender systems, while prevalent, often fail to accurately capture user…
Continue readingThe paper proposes a federated heterogeneous graph neural network (FedHGNN) for privacy-preserving recommendations. The authors suggest that the Heterogeneous Information Network (HIN) is partitioned into private HINs stored on the…
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