10.1k citations · 21.9k across the 280 of their papers we have counts for
23 papers · 1 filter
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
Haoran Yang, Hongxu Chen, Lin Li +2
User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via le…
Dual Adversarial Variational Embedding for Robust Recommendation
Qiaomin Yi, Ning Yang, Philip S. Yu
Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed. One is based on noise injection, and…
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
Zhiwei Liu, Ziwei Fan, Yu Wang +1
Sequential Recommendation characterizes the evolving patterns by modeling item sequences chronologically. The essential target of it is to capture the item transition correlations.…
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
Zi-Yuan Hu, Jin Huang, Zhi-Hong Deng +4
Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF method…
Dynamic Graph Collaborative Filtering
Xiaohan Li, Mengqi Zhang, Shu Wu +3
Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and i…
Deoscillated Graph Collaborative Filtering
Zhiwei Liu, Lin Meng, Fei Jiang +2
Collaborative Filtering (CF) signals are crucial for a Recommender System~(RS) model to learn user and item embeddings. High-order information can alleviate the cold-start issue of…