3.2k citations · 12k across the 88 of their papers we have counts for
30 papers · 1 filter
Diffusion Recommender Model
Wenjie Wang, Yiyan Xu, Fuli Feng +3
Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. Howe…
GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Wei Yinwei, Wang Xiang, Nie Liqiang +2
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interac…
Hierarchical User Intent Graph Network forMultimedia Recommendation
Wei Yinwei, Wang Xiang, He Xiangnan +3
In this work, we aim to learn multi-level user intents from the co-interacted patterns of items, so as to obtain high-quality representations of users and items and further enhance…
Why Do We Click: Visual Impression-aware News Recommendation
Jiahao Xun, Shengyu Zhang, Zhou Zhao +7
There is a soaring interest in the news recommendation research scenario due to the information overload. To accurately capture users' interests, we propose to model multi-modal fe…
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation
Shengyu Zhang, Dong Yao, Zhou Zhao +2
Learning user representations based on historical behaviors lies at the core of modern recommender systems. Recent advances in sequential recommenders have convincingly demonstrate…
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…