3 citations · 7 across the 7 of their papers we have counts for
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cs.LG2024★ 1 cited
EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration
Allen Nie, Yi Su, Bo Chang +4
Despite their success in many domains, large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. This is crucial as many r…
cs.IR2024
LLMs for User Interest Exploration in Large-scale Recommendation Systems
Jianling Wang, Haokai Lu, Yifan Liu +9
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel u…
cs.IR2024★ 3 cited
Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems
Yuyan Wang, Cheenar Banerjee, Samer Chucri +4
It has become increasingly clear that recommender systems that overly focus on short-term engagement prevents users from exploring diverse interests, ultimately hurting long-term u…