3 citations · 4 across the 6 of their papers we have counts for
6 papers
Rethinking Item Tokenization in Generative Recommenders: From Fixed Atoms to Semantic Subwords
Xinrui Miao, Mingjia Yin, Jiaqing Zhang +5
In generative recommender systems, items are typically tokenized into fixed-length semantic ID sequences for autoregressive next-item prediction. However, for user-context modeling…
Generative Data Transformation: From Mixed to Unified Data
Jiaqing Zhang, Mingjia Yin, Hao Wang +6
Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start,…
DIET: Learning to Distill Dataset Continually for Recommender Systems
Jiaqing Zhang, Hao Wang, Mingjia Yin +6
Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, ret…
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
Jiaqing Zhang, Mingjia Yin, Hao Wang +5
In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on l…
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation
Tingjia Shen, Hao Wang, Jiaqing Zhang +5
Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Trad…
A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation
Luankang Zhang, Hao Wang, Suojuan Zhang +5
Cross-domain recommendation (CDR), aiming to extract and transfer knowledge across domains, has attracted wide attention for its efficacy in addressing data sparsity and cold-start…