154 citations · 510 across the 55 of their papers we have counts for
6 papers · 1 filter
Collaborative Group-Aware Hashing for Fast Recommender Systems
Yan Zhang, Li Deng, Lixin Duan +2
The fast online recommendation is critical for applications with large-scale databases; meanwhile, it is challenging to provide accurate recommendations in sparse scenarios. Hash t…
Coherence-guided Preference Disentanglement for Cross-domain Recommendations
Zongyi Xiang, Yan Zhang, Lixin Duan +2
Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data.…
Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search
Kim Yong Tan, Yueming Lyu, Yew Soon Ong +1
Approximate nearest neighbour (ANN) search is an essential component of search engines, recommendation systems, etc. Many recent works focus on learning-based data-distribution-dep…
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations
Yan Zhang, Changyu Li, Ivor W. Tsang +5
Cold-start issues have been more and more challenging for providing accurate recommendations with the fast increase of users and items. Most existing approaches attempt to solve th…
Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation
Yan Zhang, Ivor W. Tsang, Lixin Duan
Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce. Most existing studies proposed to alleviate the cold-start problem are also k…
Deep Pairwise Hashing for Cold-start Recommendation
Yan Zhang, Ivor W. Tsang, Hongzhi Yin +3
Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focu…