4 papers · 1 filter
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…
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…
Dual prototype attentive graph network for cross-market recommendation
Li Fan, Menglin Kong, Yang Xiang +2
Cross-market recommender systems (CMRS) aim to utilize historical data from mature markets to promote multinational products in emerging markets. However, existing CMRS approaches…
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…