538 citations · 2.4k across the 71 of their papers we have counts for
12 papers · 1 filter
Denoising Multi-modal Sequential Recommenders with Contrastive Learning
Dong Yao, Shengyu Zhang, Zhou Zhao +5
There is a rapidly-growing research interest in engaging users with multi-modal data for accurate user modeling on recommender systems. Existing multimedia recommenders have achiev…
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Shengyu Zhang, Bofang Li, Dong Yao +7
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial reco…
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Shengyu Zhang, Lingxiao Yang, Dong Yao +5
Effectively representing users lie at the core of modern recommender systems. Since users' interests naturally exhibit multiple aspects, it is of increasing interest to develop mul…
Multi-trends Enhanced Dynamic Micro-video Recommendation
Yujie Lu, Yingxuan Huang, Shengyu Zhang +4
The explosively generated micro-videos on content sharing platforms call for recommender systems to permit personalized micro-video discovery with ease. Recent advances in micro-vi…
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…
Future-Aware Diverse Trends Framework for Recommendation
Yujie Lu, Shengyu Zhang, Yingxuan Huang +4
In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between hi…
A Generic Network Compression Framework for Sequential Recommender Systems
Yang Sun, Fajie Yuan, Min Yang +3
Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. Current state-of-the-art sequ…
Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos
Zhu Zhang, Zhijie Lin, Zhou Zhao +1
Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one a…
Textually Guided Ranking Network for Attentional Image Retweet Modeling
Zhou Zhao, Hanbing Zhan, Lingtao Meng +5
Retweet prediction is a challenging problem in social media sites (SMS). In this paper, we study the problem of image retweet prediction in social media, which predicts the image s…
Question Retrieval for Community-based Question Answering via Heterogeneous Network Integration Learning
Zheqian Chen, Chi Zhang, Zhou Zhao +1
Community based question answering platforms have attracted substantial users to share knowledge and learn from each other. As the rapid enlargement of CQA platforms, quantities of…
User Personalized Satisfaction Prediction via Multiple Instance Deep Learning
Zheqian Chen, Ben Gao, Huimin Zhang +2
Community based question answering services have arisen as a popular knowledge sharing pattern for netizens. With abundant interactions among users, individuals are capable of obta…