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
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
F$^2$STNet is proposed, a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA).
Jia-Yi Zhang, Jin-Feng Xu, Hewei Wang et al.· 0 citations
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