1k citations · 2.7k across the 52 of their papers we have counts for
12 papers · 1 filter
A vertex similarity index for better personalized recommendation
Ling-Jiao Chen, Zi-Ke Zhang, Jin-Hu Liu +2
Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized re…
ILCR: Item-based Latent Factors for Sparse Collaborative Retrieval
Lu Yu, Junming Huang, Chuang Liu +1
Interactions between search and recommendation have recently attracted significant attention, and several studies have shown that many potential applications involve with a joint p…
Promoting cold-start items in recommender systems
Jin-Hu Liu, Tao Zhou, Zi-Ke Zhang +3
As one of major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online.…
Multi-Linear Interactive Matrix Factorization
Lu Yu, Chuang Liu, Zi-Ke Zhang
Recommender systems, which can significantly help users find their interested items from the information era, has attracted an increasing attention from both the scientific and app…
Information Filtering via Collaborative User Clustering Modeling
Chu-Xu Zhang, Zi-Ke Zhang, Lu Yu +3
The past few years have witnessed the great success of recommender systems, which can significantly help users find out personalized items for them from the information era. One of…
A two-step Recommendation Algorithm via Iterative Local Least Squares
Jinhu Liu, Chengcheng Yang, Zi-Ke Zhang
Recommender systems can change our life a lot and help us select suitable and favorite items much more conveniently and easily. As a consequence, various kinds of algorithms have b…