4 papers · 1 filter
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…
Rethinking Fairness in LLM-Based Recommender Systems: A Survey
Song-Duo Ma, Chu-Yun Chen, Bang-An Li +3
Large Language Models (LLMs) are reshaping recommender systems by enabling more semantic, generative, and interactive recommendation pipelines. However, this shift also introduces…
Beyond Uniform Token Training: A Multi-Target Framework for Learning Token-Weighted Objectives in Generative Recommenders
Wei-Ning Chiu, Song-Duo Ma, Han-Jay Shu +2
Recent generative recommendation models recast next-item prediction as the generation of a semantic identifier sequence. While this formulation enables autoregressive models to pro…