Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Design and Implementation of Collaborative Filtering Application System using Apache Mahout -Focusing on Movie Recommendation System-

  • Lee, Jun-Ho (Dept. of Computer Science, Soonchunhyang University) ;
  • Joo, Kyung-Soo (Dept. of Computer Software Engineering, Soonchunhyang University)
  • Received : 2017.06.05
  • Accepted : 2017.07.06
  • Published : 2017.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

Keywords

References

  1. Daniel Billsus and Michael J. Pazzani, "Learning collaborative information filters," In Proceedings of the 15th International Conference on Machine Learning, ICML'98, pp. 46-54, 1998.
  2. Xiaoyuan Su and Taghi M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, pp.1-19, 2009.
  3. Mingun Kim, and Kyoung-Jae Kim, "Recommender Systems using Structural Hole and Collaborative Filtering." Journal of Intelligence and Information Systems 20.4, 107-120, 2014. https://doi.org/10.13088/jiis.2014.20.4.107
  4. Lee, Seok-Jun, and Hee-Choon Lee, "A study on the Prediction Performance of the Correspondence Mean Algorithm in Collaborative Filtering Recommendation." Information Systems Review 9, 2007.
  5. Su-Mi Shin, Kyung-Chang Kim, "Addressing the New User Problem of Recommender Systems Based on Word Embedding Learning and Skip-gram Modelling," Journal of The Korea Society of Computer and Information, Vol. 21, No. 7, pp. 9-16, 2016. https://doi.org/10.9708/JKSCI.2016.21.7.009
  6. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., "GroupLens: an open architecture for collaborative filtering of netnews," In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175-186, October, 1994.
  7. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Item-based collaborative filtering recommendation algorithms," In Proceedings of the 10th international conference on World Wide Web, ACM, pp. 285-295, April, 2001.
  8. ZHAO, Zhi-Dan, SHANG, Ming-Sheng. "User-based collaborative-filtering recommendation algorithms on hadoop," In: Knowledge Discovery and Data Mining, 2010. WKDD'10. Third International Conference on. IEEE, pp. 478-481, 2010.
  9. Yu, K., Schwaighofer, A., Tresp, V., Xu, X., & Kriegel, H. P., "Probabilistic memory-based collaborative filtering," IEEE Transactions on Knowledge and Data Engineering, pp. 56-69, 2004.
  10. Y. Ding, "Time weight collaborative filtering," Proceedings of the 14th ACM international conference, pp.485-492, 2005.
  11. S. Owen, R. Anil, T. Dunning and E. Friedman, "Mahout in Action" ManningPublications, 2014.
  12. The Apache Foundation, https://github.com/apache/mahout/blob/trunk/mrlegacy/src/main/java/org/apache/mahout/cf/taste/impl/recommender/
  13. Freeman Eric, Freeman Elisabeth, Sierra Kathy and Bates Bert, "Head First Design Patterns" Oreilly & Associates Inc, pp. 529-558, 2004
  14. Joo, K. S. and Woo, J. W., "A Development of the Unified Object-Oriented Analysis and Design Methodology for Security-Critical Web Applications Based on Object-Relational Database-Forcusing on Oracle11g," Journal of The Korea Society of Computer and Information, 17(12), pp. 169-177, 2012. https://doi.org/10.9708/jksci/2012.17.12.169
  15. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Application of dimensionality reduction in recommender system-a case study," Minnesota Univ Minneapolis Dept of Computer Science, No. TR-00-043, 2000.
  16. Yang, Y. and Liu, X., "A re-examination of text categorization methods," In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp. 42-49, August, 1999.