45 citations · 60 across the 7 of their papers we have counts for
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cs.IR2023★ 1 cited
Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems
Pan Li, Yuyan Wang, Ed H. Chi +1
Recommending novel content, which expands user horizons by introducing them to new interests, has been shown to improve users' long-term experience on recommendation platforms \cit…
cs.IR2023★ 4 cited
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations
Pan Li, Yuyan Wang, Ed H. Chi +1
Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit…
cs.IR2022
Learning to Augment for Casual User Recommendation
Jianling Wang, Ya Le, Bo Chang +3
Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large bod…