281 citations · 665 across the 22 of their papers we have counts for
12 papers · 1 filter
UGRec: Modeling Directed and Undirected Relations for Recommendation
Xinxiao Zhao, Zhiyong Cheng, Lei Zhu +2
Recommender systems, which merely leverage user-item interactions for user preference prediction (such as the collaborative filtering-based ones), often face dramatic performance d…
Interest-aware Message-Passing GCN for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the col…
Feature-level Attentive ICF for Recommendation
Zhiyong Cheng, Fan Liu, Shenghan Mei +3
Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender sys…
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Lei Zhu, Hui Cui, Zhiyong Cheng +2
Social network stores and disseminates a tremendous amount of user shared images. Deep hashing is an efficient indexing technique to support large-scale social image retrieval, due…
Task-adaptive Asymmetric Deep Cross-modal Hashing
Fengling Li, Tong Wang, Lei Zhu +2
Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. Because of its…
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Yang Xu, Lei Zhu, Zhiyong Cheng +2
Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items…
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
As important side information, attributes have been widely exploited in the existing recommender system for better performance. In the real-world scenarios, it is common that some…
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
Xiao Dong, Lei Zhu, Xuemeng Song +2
In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific…
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
Lei Zhu, Zi Huang, Zhihui Li +2
Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency.…
Fusion-supervised Deep Cross-modal Hashing
Li Wang, Lei Zhu, En Yu +2
Deep hashing has recently received attention in cross-modal retrieval for its impressive advantages. However, existing hashing methods for cross-modal retrieval cannot fully captur…
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Zhiyong Cheng, Xiaojun Chang, Lei Zhu +2
Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for indiv…
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
Zhiyong Cheng, Ying Ding, Lei Zhu +1
Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and…