Enhancing memory-based collaborative filtering for group recommender systems

作者：

Highlights：

• Enhancing memory-based collaborative filtering techniques for group recommender systems by resolving the data sparsity problem.

• Comparing the proposed method’s accuracy with basic memory-based techniques and latent factor model.

• Makeing accurate predictions for unknown ratings in sparse matrices based on the proposed method.

• More users are satisfied of the group recommender system’s performance.

摘要

•Enhancing memory-based collaborative filtering techniques for group recommender systems by resolving the data sparsity problem.•Comparing the proposed method’s accuracy with basic memory-based techniques and latent factor model.•Makeing accurate predictions for unknown ratings in sparse matrices based on the proposed method.•More users are satisfied of the group recommender system’s performance.

论文关键词：Group recommendation system,Sparsity problem,Collaborative filtering technique,User-based approach,Item-based approach

论文评审过程：Available online 29 November 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.11.042