Sequential recommendation optimizes which items to rank, while each displayed slate also shapes subsequent feedback and user state. We study how a trained ranker can support decisions about these future consequences. We introduce UA-TWM, a utility-anchored world-model interface that constructs nearby slate actions, est...
Jin-Feng Xu, Zhe-Yu Chen, Zi-Yue Peng et al.· 0 citations
While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive memory requirements. Extracting a compact subset of experts presents a promising solution. However, existing expert selection heuristics predominantly rely on Top-k ran...
Zheng Lin, Shao-Ke Fang, Yu-Xin Zhang et al.· 0 citations
Online platforms increasingly rely on multimodal recommender systems to rank products, media, and other Web content. Existing methods usually inject visual and textual features into item representations or build homogeneous graphs from modality-level similarity, but the resulting signals can remain misaligned with the...
Jin-Feng Xu, Zhe-Yu Chen, Shuo Yang et al.· 0 citations
Recent studies in multimodal recommendation, which leverage diverse modal information to address data sparsity and enhance recommendation accuracy, have garnered significant interest. Two critical processes in this domain are modality fusion and representation learning. In representation learning, existing studies ofte...
Jin-Feng Xu, Zhe-Yu Chen, Wei Wang et al.· ACM Transactions on Recommen...· 0 citations
Graph Collaborative Filtering (GCF) has become the dominant paradigm in modern recommender systems by modeling user-item interactions as a bipartite graph and propagating embeddings through a fixed number of message-passing layers. However, applying a uniform propagation depth to every node ignores a fundamental proper...
Jinfeng Xu, Zheyu Chen, Ziyue Peng et al.· 0 citations
This paper proposes Dude, the first Dual-Detection Multi-Agent System for paper-code discrepancy detection, with a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude that effectively prevents agents from falsely reporting discrepancies.
Wei-Jie Liu, Running Zhao, Wen-Hao Yuan et al.· 0 citations
A client-specific adaptation channel based on private prompt tokens, which tracks local adaptation dynamics separately from the shared backbone and provides a lightweight signal for detecting whether client adaptation remains active, and a shallow sufficiency estimator that combines cross-client semantic alignment, tem...
Wen-Hao Yuan, Chenchen Lin, Wentao Hu et al.· 0 citations
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