437 citations · 470 across the 6 of their papers we have counts for
7 papers
Tlow: Flow-based Item Tokenizer for Recommendation
Nian Li, Chonggang Song, Jingtao Ding +3
Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundamentally addressing the problems…
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information
Chonggang Song, Chunxu Shen, Hao Gu +4
Real-world recommendation systems commonly offer diverse content scenarios for users to interact with. Considering the enormous number of users in industrial platforms, it is infea…
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
Weiqing Luo, Chonggang Song, Lingling Yi +1
Combining semantic information with behavioral data is a crucial research area in recommender systems. A promising approach involves leveraging external knowledge to enrich behavio…
Addressing Confounding Feature Issue for Causal Recommendation
Xiangnan He, Yang Zhang, Fuli Feng +4
In recommender system, some feature directly affects whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, s…
Causal Intervention for Leveraging Popularity Bias in Recommendation
Yang Zhang, Fuli Feng, Xiangnan He +4
Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (long-tail) distribution on the interaction frequency; from the method pers…
CatGCN: Graph Convolutional Networks with Categorical Node Features
Weijian Chen, Fuli Feng, Qifan Wang +4
Recent studies on Graph Convolutional Networks (GCNs) reveal that the initial node representations (i.e., the node representations before the first-time graph convolution) largely…