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20242026
most citedTowards Edge General Intelligence via Large Language Models: Opportunities and Challenges

26 citations · 75 across the 30 of their papers we have counts for

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Showing 2026 · cs.IRShow all

6 papers · 2 filters

cs.IR2026

Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation

Jinfeng Xu, Zheyu Chen, Ziyue Peng +5

Recommendation recursive self-improvement (Rec-RSI) feeds recommender outputs into subsequent training. Evaluating each round solely through its latest model assumes that the succe…

cs.IR2026

Agents as Knowledge Integrator and Utilizer in Multimodal Recommendation

Jinfeng Xu, Zheyu Chen, Shuo Yang +9

Online platforms increasingly rely on multimodal recommender systems to rank products, media, and other Web content. Existing methods usually inject visual and textual features int…

cs.IR2026

Neural Tree Collaborative Filtering: Rethinking Graph Collaborative Filtering as Tree Collaborative Filtering with Curvature-Aware Propagation Depth

Jinfeng Xu, Zheyu Chen, Ziyue Peng +5

Graph Collaborative Filtering (GCF) has become the dominant paradigm in modern recommender systems by modeling user-item interactions as a bipartite graph and propagating embedding…

cs.IR2026

One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation

Jinfeng Xu, Zheyu Chen, Ziyue Peng +6

Multimodal recommendation leverages item multimodal features alongside collaborative signals to capture user preferences. While item-item graphs have become a key building block in…

cs.IR2026

Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation

Jinfeng Xu, Zheyu Chen, Shuo Yang +6

Recent advancements in multimodal recommendations, which leverage diverse modality information to mitigate data sparsity and improve recommendation accuracy, have gained significan…

cs.IR2026

CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation

Jinfeng Xu, Zheyu Chen, Shuo Yang +6

The explosion of multimedia data in information-rich environments has intensified the challenges of personalized content discovery, positioning recommendation systems as an essenti…