2.2k citations · 3.1k across the 14 of their papers we have counts for
6 papers · 1 filter
Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation
Wei Ji, Xiangyan Liu, An Zhang +3
Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative f…
Cross Pairwise Ranking for Unbiased Item Recommendation
Qi Wan, Xiangnan He, Xiang Wang +3
Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The lo…
Disentangled Graph Collaborative Filtering
Xiang Wang, Hongye Jin, An Zhang +3
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user…
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Xiang Wang, Yaokun Xu, Xiangnan He +3
Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender…
Neural Graph Collaborative Filtering
Xiang Wang, Xiangnan He, Meng Wang +2
Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged dee…
Item Silk Road: Recommending Items from Information Domains to Social Users
Xiang Wang, Xiangnan He, Liqiang Nie +1
Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like T…