Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks
作者：
Highlights：
• We solve the data sparsity problem in recommendation via node clustering in networks.
• We use node clustering to reconstruct a denser user-item bipartite networks.
• We test a diffusion-based recommendation method in the reconstructed networks.
• Our method is validated in three benchmarked data sets.
• Recommendation in the reconstructed networks have higher accuracy and item coverage.
摘要
•We solve the data sparsity problem in recommendation via node clustering in networks.•We use node clustering to reconstruct a denser user-item bipartite networks.•We test a diffusion-based recommendation method in the reconstructed networks.•Our method is validated in three benchmarked data sets.•Recommendation in the reconstructed networks have higher accuracy and item coverage.
论文关键词：Recommender system,Sparsity,Bipartite network,Clustering nodes,Collaborative filtering
论文评审过程：Received 2 September 2019, Revised 26 February 2020, Accepted 27 February 2020, Available online 27 February 2020, Version of Record 5 March 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113346
