7 papers
When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Study
Shutong Qiao, Wei Yuan, Tong Chen +3
Semantic ID learning is a key interface in Generative Recommendation (GR) models, mapping items to discrete identifiers grounded in side information, most commonly via a pretrained…
ProEx: A Unified Framework Leveraging Large Language Model with Profile Extrapolation for Recommendation
Yi Zhang, Yiwen Zhang, Yu Wang +2
The powerful text understanding and generation capabilities of large language models (LLMs) have brought new vitality to general recommendation with implicit feedback. One possible…
Relational Database Distillation: From Structured Tables to Condensed Graph Data
Xinyi Gao, Jingxi Zhang, Lijian Chen +3
Relational databases (RDBs) underpin the majority of global data management systems, where information is structured into multiple interdependent tables. To effectively use the kno…
Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation
Yi Zhang, Yiwen Zhang, Yu Wang +2
Generative recommendation aims to learn the underlying generative process over the entire item set to produce recommendations for users. Although it leverages non-linear probabilis…
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
Hechuan Wen, Tong Chen, Guanhua Ye +3
Causal effect estimation (CEE) provides a crucial tool for predicting the unobserved counterfactual outcome for an entity. As CEE relaxes the requirement for ``perfect'' counterfac…
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
Xinyi Gao, Yayong Li, Tong Chen +3
With the increasing computation of training graph neural networks (GNNs) on large-scale graphs, graph condensation (GC) has emerged as a promising solution to synthesize a compact,…