6 citations · 9 across the 4 of their papers we have counts for
4 papers
Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
Chu-Jen Shao, Hao-Ming Fu, Pu-Jen Cheng
In the one-class recommendation problem, it's required to make recommendations basing on users' implicit feedback, which is inferred from their action and inaction. Existing works…
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis
Hao-Ming Fu, Pu-Jen Cheng
Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for v…
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…