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...
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| Published in | TheScientificWorld Vol. 2013; no. 2013; pp. 1 - 9 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2013
John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2356-6140 1537-744X 1537-744X |
| DOI | 10.1155/2013/869658 |
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| 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. |
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| AbstractList | 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. 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 A-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by A-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. 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.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. 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. |
| Audience | Academic |
| Author | Xu, Chonghuan Ju, Chunhua |
| AuthorAffiliation | 1 Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China 3 College of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China 2 College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China |
| AuthorAffiliation_xml | – name: 1 Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China – name: 2 College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China – name: 3 College of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China |
| Author_xml | – sequence: 1 fullname: Ju, Chunhua – sequence: 2 fullname: Xu, Chonghuan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24381525$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1145/2382438.2382442 10.1023/A:1006544522159 10.1016/j.patcog.2006.12.019 10.1016/j.ipm.2013.02.004 10.1007/978-3-540-72950-1_77 10.1016/j.asoc.2011.11.016 10.1016/j.datak.2005.05.009 10.1016/j.physa.2008.10.010 10.1016/j.eswa.2013.01.004 10.1145/245108.245124 10.1103/PhysRevLett.87.248701 10.1016/j.eswa.2011.08.141 10.1016/j.knosys.2012.07.019 10.1209/0295-5075/82/58007 10.1016/j.eswa.2012.01.112 10.1016/j.asoc.2009.12.025 10.1155/2013/739460 10.1145/963770.963775 10.1016/j.jnca.2013.02.016 10.1209/0295-5075/80/68003 10.1016/j.ipm.2012.07.008 10.1016/j.physa.2010.04.004 10.1016/j.dss.2011.01.012 10.1145/963770.963772 10.1007/s10898-007-9149-x |
| ContentType | Journal Article |
| Copyright | Copyright © 2013 Chunhua Ju and Chonghuan Xu. COPYRIGHT 2013 John Wiley & Sons, Inc. Copyright © 2013 Chunhua Ju and Chonghuan Xu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2013 C. Ju and C. Xu. 2013 |
| Copyright_xml | – notice: Copyright © 2013 Chunhua Ju and Chonghuan Xu. – notice: COPYRIGHT 2013 John Wiley & Sons, Inc. – notice: Copyright © 2013 Chunhua Ju and Chonghuan Xu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2013 C. Ju and C. Xu. 2013 |
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| Snippet | Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill... |
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| SubjectTerms | Accuracy Algorithms Animals Bees - physiology Brand loyalty Cluster Analysis Clustering Clustering (Computers) Collaboration Cooperative Behavior Datasets Filtration Information processing Mathematical optimization Mathematical research Methods Numerical analysis Recommender systems Search algorithms Similarity Similarity measures Swarm intelligence Vector quantization |
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| Title | A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm |
| URI | https://search.emarefa.net/detail/BIM-1013066 https://dx.doi.org/10.1155/2013/869658 https://www.ncbi.nlm.nih.gov/pubmed/24381525 https://www.proquest.com/docview/2175224890 https://www.proquest.com/docview/1490718751 https://pubmed.ncbi.nlm.nih.gov/PMC3863462 https://downloads.hindawi.com/journals/tswj/2013/869658.pdf https://doaj.org/article/63feec0ca30c4c4baa525fb791941580 |
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