title: A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.
abstract: Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.
authors.affiliation: Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China ; College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Ju
authors.affiliation: Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China ; College of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China.
authors.forename: Chonghuan
authors.initials: C
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D000818:Animals; D001516:Bees / Q000502:physiology*; D016000:Cluster Analysis; D003299:Cooperative Behavior