5 papers · 1 filter
Controllable Generative Recommendation via Guided Token Refinement
Haichao Zhang, Zhixuan Liang, Chong Zhang +3
Generative recommendation over discrete Semantic IDs has become a powerful paradigm for item retrieval. Real-world systems, however, frequently require real-time controllability to…
Bridging Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation
Zhida Qin, Zemu Liu, Haoyan Fu +4
Cross-domain sequential recommendation (CDSR) alleviates interaction sparsity by jointly modeling user behaviors across multiple domains. While current studies have made some progr…
InfoDCL: Informative Noise Enhanced Diffusion Based Contrastive Learning
Xufeng Liang, Zhida Qin, Chong Zhang +2
Contrastive learning has demonstrated promising potential in recommender systems. Existing methods typically construct sparser views by randomly perturbing the original interaction…
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…