6 citations · 11 across the 8 of their papers we have counts for
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Are We Really Making Progress in Group Recommendation? Unmasking the Tie-Breaking Illusion
Song-Duo Ma, Pu-Jen Cheng
Recent group recommendation methods have reported strong improvements on standard benchmarks, but it remains unclear whether these gains always reflect genuine advances in modeling…
Structure-Preserving Projection for Mitigating Modality Bias in LLM-Based Sequential Recommendation
Tzu-Wei Chiu, Song-Duo Ma, Hsin-Yu Lin +1
Recent LLM-based recommenders integrate textual and collaborative signals by projecting collaborative embeddings into the embedding space of the LLM. However, this projection can i…
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…
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…
Mr. Right: Multimodal Retrieval on Representation of ImaGe witH Text
Cheng-An Hsieh, Cheng-Ping Hsieh, Pu-Jen Cheng
Multimodal learning is a recent challenge that extends unimodal learning by generalizing its domain to diverse modalities, such as texts, images, or speech. This extension requires…