1.5k citations · 6k across the 75 of their papers we have counts for
24 papers · 1 filter
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studi…
Relation-aware Heterogeneous Graph for User Profiling
Qilong Yan, Yufeng Zhang, Qiang Liu +2
User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entitie…
Fully Hyperbolic Graph Convolution Network for Recommendation
Liping Wang, Fenyu Hu, Shu Wu +1
Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perfor…
Mining Latent Structures for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommen…
Dynamic Graph Neural Networks for Sequential Recommendation
Mengqi Zhang, Shu Wu, Xueli Yu +2
Modeling user preference from his historical sequences is one of the core problems of sequential recommendation. Existing methods in this field are widely distributed from conventi…
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
Xueli Yu, Weizhi Xu, Zeyu Cui +2
The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem…
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
Yufeng Zhang, Jinghao Zhang, Zeyu Cui +2
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task a…
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…
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
Yichen Xu, Yanqiao Zhu, Feng Yu +2
Click-Through Rate (CTR) prediction, whose aim is to predict the probability of whether a user will click on an item, is an essential task for many online applications. Due to the…
Heterogeneous Graph Collaborative Filtering
Zekun Li, Yujia Zheng, Shu Wu +2
Graph-based collaborative filtering (CF) algorithms have gained increasing attention. Existing work in this literature usually models the user-item interactions as a bipartite grap…
Disentangled Item Representation for Recommender Systems
Zeyu Cui, Feng Yu, Shu Wu +2
Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vect…
TFNet: Multi-Semantic Feature Interaction for CTR Prediction
Shu Wu, Feng Yu, Xueli Yu +5
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems. There exists several kinds of methods proposed in t…
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Feng Yu, Yanqiao Zhu, Qiang Liu +3
Session-based recommendation nowadays plays a vital role in many websites, which aims to predict users' actions based on anonymous sessions. There have emerged many studies that mo…
Learning Preferences and Demands in Visual Recommendation
Qiang Liu, Shu Wu, Liang Wang
Visual information is an important factor in recommender systems, in which users' selections consist of two components: \emph{preferences} and \emph{demands}. Some studies has been…
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
Mengqi Zhang, Shu Wu, Meng Gao +3
The problem of session-aware recommendation aims to predict users' next click based on their current session and historical sessions. Existing session-aware recommendation methods…
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
Zekun Li, Zeyu Cui, Shu Wu +2
Click-through rate (CTR) prediction is an essential task in web applications such as online advertising and recommender systems, whose features are usually in multi-field form. The…
Semi-supervised Compatibility Learning Across Categories for Clothing Matching
Zekun Li, Zeyu Cui, Shu Wu +2
Learning the compatibility between fashion items across categories is a key task in fashion analysis, which can decode the secret of clothing matching. The main idea of this task i…
Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
Zeyu Cui, Zekun Li, Shu Wu +2
With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of…
Session-based Recommendation with Graph Neural Networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu +3
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representatio…
A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
Qiang Cui, Shu Wu, Yan Huang +1
Sequential recommendation is one of fundamental tasks for Web applications. Previous methods are mostly based on Markov chains with a strong Markov assumption. Recently, recurrent…
Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach
Qiang Liu, Feng Yu, Shu Wu +1
With the rapid growth of social media, massive misinformation is also spreading widely on social media, such as microblog, and bring negative effects to human life. Nowadays, autom…
MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
Qiang Cui, Shu Wu, Qiang Liu +2
Sequential recommendation is a fundamental task for network applications, and it usually suffers from the item cold start problem due to the insufficiency of user feedbacks. There…
Context-aware Sequential Recommendation
Qiang Liu, Shu Wu, Diyi Wang +2
Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption ar…
Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Model
Qiang Liu, Shu Wu, Liang Wang
With the rapid growth of Internet applications, sequential prediction in collaborative filtering has become an emerging and crucial task. Given the behavioral history of a specific…