20 citations · 24 across the 4 of their papers we have counts for
5 papers
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
Sirui Chen, Shen Han, Jiawei Chen +6
Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of R…
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
Weiqin Yang, Jiawei Chen, Xin Xin +5
Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant lim…
The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective
Binbin Hu, Weifan Wang, Hanshu Wang +4
Cross-domain Recommendation (CDR) aims to alleviate the data sparsity and the cold-start problems in traditional recommender systems by leveraging knowledge from an informative sou…
Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
Zhe Wang, Sheng Zhou, Jiawei Chen +5
Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling compl…
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Siyi Lin, Chongming Gao, Jiawei Chen +5
Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this…