6 citations · 7 across the 5 of their papers we have counts for
6 papers
Semantic Item Graph Enhancement for Multimodal Recommendation
Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng +2
Multimodal recommendation systems have attracted increasing attention for their improved performance by leveraging items' multimodal information. Prior methods often build modality…
CM: Calibrating Multimodal Recommendation
Xin Zhou, Yongjie Wang, Zhiqi Shen
Alignment and uniformity are fundamental principles within the domain of contrastive learning. In recommender systems, prior work has established that optimizing the Bayesian Perso…
Response Uncertainty and Probe Modeling: Two Sides of the Same Coin in LLM Interpretability?
Yongjie Wang, Yibo Wang, Xin Zhou +1
Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. However, the factors governing a dat…
Learning Item Representations Directly from Multimodal Features for Effective Recommendation
Xin Zhou, Xiaoxiong Zhang, Dusit Niyato +1
Conventional multimodal recommender systems predominantly leverage Bayesian Personalized Ranking (BPR) optimization to learn item representations by amalgamating item identity (ID)…
GiFT: Gibbs Fine-Tuning for Code Generation
Haochen Li, Wanjin Feng, Xin Zhou +1
Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. A key approach is self-training, where LLMs are iteratively trained on self-ge…
Advancing Sustainability via Recommender Systems: A Survey
Xin Zhou, Lei Zhang, Honglei Zhang +4
Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to tra…