Browse > Article
http://dx.doi.org/10.7236/JIIBC.2014.14.1.101

Distributed Recommendation System Using Clustering-based Collaborative Filtering Algorithm  

Jo, Hyun-Je (Dept. of computer engineering, Inha University)
Rhee, Phill-Kyu (Dept. of computer engineering, Inha University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.14, no.1, 2014 , pp. 101-107 More about this Journal
Abstract
This paper presents an efficient distributed recommendation system using clustering collaborative filtering algorithm in distributed computing environments. The system was built based on Hadoop distributed computing platform, where distributed Min-hash clustering algorithm is combined with user based collaborative filtering algorithm to optimize recommendation performance. Experiments using Movie Lens benchmark data show that the proposed system can reduce the execution time for recommendation compare to sequential system.
Keywords
Recommendation System; Collaborative Filtering; Min-hash Clustering; Hadoop;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1)
2 Paul Renick and Hal R. Varian, "Recommender System," Communications of the ACM" Vol 40, No.3, March. 1997
3 Yan Shen, Hak-Chul Shin, "Reinforcement Learning Algorithm Based Hybrid Filtering Image Recommender System",The Journal of The Institute of Internet, Broadcasting and Communication, VOl. 12 No. 3, June 2012
4 Su, X., & Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, 2009(12)
5 Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007). Google news personalization: scalable online collaborative filtering, 271-280.
6 http://hadoop.apache.org/
7 http://grouplens.org/datasets/movielens/
8 Gong, S. (2010). A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software, 5(7), 745-752. doi:10.4304/jsw.5.7.745-752   DOI
9 G Smith Linden B.; York, J. (n.d.). Amazon.com Recommendations: Item-to-item Collaborative Filtering.
10 Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., et al. (2010). The YouTube video recommendation system, 293-296. doi:10.1145/1787275.1787324
11 Karypis, G. "Evaluation of item-based top-n recommendation algorithms", in Proceedings of the International Conferrence on Information and Knowledge Management (CIKM'01), pp.247-254, Atlanta, Ga, USA, November 2001.
12 http://www.netflix.com/
13 https://news.google.com/
14 http://www.youtube.com/
15 H. Lee, J. Kwon, "A New Distributed Graph Data Storage System for Large-Scale Recommender Engines", Journal of Korean Institute of Information Technology, Vol. 11, No. 7, pp. 139-149, July 31, 2013.
16 Seok-Jong Yu, "Comprehensive Temporal Filter for Expanded Collaborative Filtering Algorithm", Journal of Korean Institute of Information Technology, Vol. 11, No. 11, pp. 173-179, Nov. 30, 2013.
17 Kitae Hwang, "Genre-based Collaborative Filtering Movie Recommendation",The Journal of The Institute of Internet, Broadcasting and Communication, VOl. 10, No. 3, June 2010