6 citations · 11 across the 8 of their papers we have counts for
3 papers · 1 filter
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
Hao-Lun Lin, Jyun-Yu Jiang, Ming-Hao Juan +1
Nowadays, modern recommender systems usually leverage textual and visual contents as auxiliary information to predict user preference. For textual information, review texts are one…
Attentive Graph-based Text-aware Preference Modeling for Top-N Recommendation
Ming-Hao Juan, Pu-Jen Cheng, Hui-Neng Hsu +1
Textual data are commonly used as auxiliary information for modeling user preference nowadays. While many prior works utilize user reviews for rating prediction, few focus on top-N…
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Guan-Ting Liu, En-Pei Hu, Pu-Jen Cheng +2
Aiming to produce reinforcement learning (RL) policies that are human-interpretable and can generalize better to novel scenarios, Trivedi et al. (2021) present a method (LEAPS) tha…