112 citations · 175 across the 10 of their papers we have counts for
6 papers · 1 filter
Cold-start Sequential Recommendation via Meta Learner
Yujia Zheng, Siyi Liu, Zekun Li +1
This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based…
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…
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
Yujia Zheng, Siyi Liu, Zekun Li +1
The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to captur…
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…