collaborators

19 papers

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.CL2026

LATTE: Forecasting Peer Anchored Preference Trajectories for Personalized LLM Generation

Jinze Li, Xiaoyan Yang, Shuo Yang +5

Personalized generation with frozen large language models requires a conditioning signal that is both compact and current. Existing personalization methods typically retrieve or su…

cs.CL2026

Beyond the Target: From Imitation to Collaboration in Speculative Decoding

Jinze Li, Yixing Xu, Guanchen Li +7

Speculative decoding (SPD) accelerates large language model (LLM) inference by letting a smaller draft model propose multiple future tokens that are verified in parallel by a large…

cs.CL2026

OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory

Jinze Li, Yang Zhang, Xin Yang +5

Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended histories. However, existing…

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…