VQV Signal
MATE introduces adaptive long- and short-term user memory to improve LLM-based recommender systems by distinguishing persistent preferences from recent interests. This approach addresses limitations in semantic representations for personalized recommendations.
MATE introduces adaptive long- and short-term user memory to improve LLM-based recommender systems by distinguishing persistent preferences from recent interests. This approach addresses limitations in semantic representations for personalized recommendations.
AI-assisted summary based on the listed source.
Large language model (LLM)-enhanced recommender systems leverage rich item semantics to support personalized recommendation. However, semantic representations alone do not determine which historical behaviors reflect persistent preferences and which mainly indicate recent interests, leaving an important aspect of...
Understanding the difference between long-term and short-term user behaviors can significantly improve recommendation accuracy. MATE's method leverages advances in LLM inference to better capture user intent over time.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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