166 citations · 346 across the 9 of their papers we have counts for
11 papers
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…
Label Denoising through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +2
Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optim…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall acc…
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
Zhaochun Ren, Na Huang, Yidan Wang +7
Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumul…
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
Jiajia Chen, Xin Xin, Xianfeng Liang +2
Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented…
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis +2
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms…
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +3
Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while…
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu +5
Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causin…
Graph Convolutional Embeddings for Recommender Systems
Paula Gómez Duran, Alexandros Karatzoglou, Jordi Vitrià +2
Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data. The main signal to analyze stems from the…
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Fuli Feng, Weiran Huang, Xiangnan He +3
Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are n…