3 papers
cs.IR2026
A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
Yang Xiang, Li Fan, Chenke Yin +2
Foundation models in language and vision benefit from a unified discrete token interface that converts raw inputs into sequences for scalable pre-training and inference. For graphs…
cs.IR2025
Uncertainty-Aware Semantic Decoding for LLM-Based Sequential Recommendation
Chenke Yin, Li Fan, Jia Wang +4
Large language models have been widely applied to sequential recommendation tasks, yet during inference, they continue to rely on decoding strategies developed for natural language…
cs.IR2025
Harnessing Light for Cold-Start Recommendations: Leveraging Epistemic Uncertainty to Enhance Performance in User-Item Interactions
Yang Xiang, Li Fan, Chenke Yin +2
Most recent paradigms of generative model-based recommendation still face challenges related to the cold-start problem. Existing models addressing cold item recommendations mainly…