6 citations · 11 across the 8 of their papers we have counts for
10 papers
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…
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…
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…
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…
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex
Chun-Hsien Lin, Pu-Jen Cheng
Word embedding is a modern distributed word representations approach widely used in many natural language processing tasks. Converting the vocabulary in a legal document into a wor…
End-to-End Video Question-Answer Generation with Generator-Pretester Network
Hung-Ting Su, Chen-Hsi Chang, Po-Wei Shen +5
We study a novel task, Video Question-Answer Generation (VQAG), for challenging Video Question Answering (Video QA) task in multimedia. Due to expensive data annotation costs, many…