A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.
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. I...
| Published in: | Scientific World Journal pp. 869658 - 869659 |
|---|---|
| Main Authors: | , |
| Format: | research Journal Article |
| Published: |
Wiley-Blackwell
2013
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104135983&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104135983 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104135983 NLM24381525 2012423632 10.1155/2013/869658 NLM24381525 PMC3863462 104135983 ppf: 869658 ppct: 1 formats: tig: atl: A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm. aug: au: Ju, Chunhua Xu, Chonghuan affil: Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China ; College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China. sug: subj: Algorithms Bees and Wasps Physiology Cooperative Behavior Animal Studies Cluster Analysis ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|