Improving recommendation quality and user satisfaction is critical to the success of recommendation platforms. Prior studies have attempted to enhance user experience by collecting user feedback through surveys and modeling their responses accordingly. However, the inherent noise in survey responses presents a signific...
Haoze Wu, Jian Ding, Chenghui Yu et al.· Proceedings of the 32nd ACM...· 0 citations
Looped language models separate computational depth from parameter count by repeatedly applying the same transformer block. Adapting these models requires a shared update that remains effective as hidden states evolve throughout the recurrent computation. Our empirical analysis reveals a pronounced late-loop bias in st...
Zi-Rui Zhu, Hai-Lun Xu, Xuan-Lei Zhao et al.· 0 citations
Recent advances in generative modeling have reshaped recommender systems by formulating recommendation as a next-item generation problem. Existing retrieval approaches primarily follow two paradigms: user-to-item (U2I) methods represent user context using one or a few deterministic embeddings, which limits the ability...
Cheng-Lei Shen, Chen-Zhe Huang, Dong Jiang et al.· 0 citations
Large-scale video retrieval requires embedding models to encode long and diverse videos under tight visual-input and inference budgets. Existing methods typically sample a small, fixed set of frames at their original resolution, limiting temporal coverage and ignoring frame importance. Our empirical analysis shows that...
Song Jin, Zhong-Tao Jiang, Cheng-Lei Shen et al.· 0 citations
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