173 citations · 211 across the 7 of their papers we have counts for
7 papers
Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning
Wenhui Yu, Zheng Qin
Implicit feedback data is extensively explored in recommendation as it is easy to collect and generally applicable. However, predicting users' preference on implicit feedback data…
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
Wenhui Yu, Xiao Lin, Junfeng Ge +2
Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designi…
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Wenhui Yu, Zheng Qin
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the gr…
Spectrum-enhanced Pairwise Learning to Rank
Wenhui Yu, Zheng Qin
To enhance the performance of the recommender system, side information is extensively explored with various features (e.g., visual features and textual features). However, there ar…
Efficient Contour Computation of Group-based Skyline
Wenhui Yu, Jinfei Liu, Jian Pei +3
Skyline, aiming at finding a Pareto optimal subset of points in a multi-dimensional dataset, has gained great interest due to its extensive use for multi-criteria analysis and deci…
Visually-aware Recommendation with Aesthetic Features
Wenhui Yu, Xiangnan He, Jian Pei +4
Visual information plays a critical role in human decision-making process. While recent developments on visually-aware recommender systems have taken the product image into account…
Aesthetic-based Clothing Recommendation
Wenhui Yu, Huidi Zhang, Xiangnan He +3
Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decisio…