IJSRSET calls volunteers interested to contribute towards the scientific development in the field of Science, Engineering and Technology

Home > IJSRSET162539                                                     


Service Recommendation Using Rating Inference Approach

Authors(2):

Yamini Nikam, M. B. Vaidya
  • Abstract
  • Authors
  • Keywords
  • References
  • Details
There is large amount of information available on World Wide Web. But all information is not relevant to a particular user. So this is the place where recommender system is useful. Recommender system guides the user. But traditional recommender systems has some limitations. So, to overcome this limitations we are using a hybrid algorithm which gives accurate result. Which combines the collaborative filtering with sentimental analysis. Therefore, implementing the algorithm distributed will reduce the required computing time. Apache Hadoop is an open-source distributed computing framework that can be composed of a large number of low-cost hardware to run the application on a cluster. It provides applications with a set of stable and reliable interface, Use of single recommended method is difficult to meet the demand of a large amount of data and accuracy requirements.

Yamini Nikam, M. B. Vaidya

Recommender System, Preferences, keyword, Map-Reduce, Top Rank.

  1. Kai Yu, Xiaowei Xu, Jianhua Tao, Martin Ester,Hans-Peter Kriegel “Instance selection techniques for memory based collaborative filtering”, Proc. Second SIAM International Conference on Data Mining (SDM'02)
  2. Prem Melville and Vikas Sindhwani, “Recommender Systems”, Encyclopedia of Machine Learning, 2010.
  3. John S. Breese, David Heckerman, and Carl Kadie (1998). "Empirical analysis of predictive algorithms for collaborative filtering". In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (UAI'98).
  4. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., et al. (July 1999). “Combining collaborative filtering with personal agents for better recommendations”. In Proceedings of the sixteenth national conference on artificial intelligence (AAAI-99), Orlando, Florida (pp. 439-436).
  5. Xiaoyuan Su, Taghi M. Khoshgoftaar, “A survey of collaborative filtering techniques”, Advances in Artificial Intelligence archive, 2009.
  6. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., & Reidl, J. (1994a). “GroupLens: An open architecture for collaborative filtering of netnews”. In Proceedings of the 1994 computer supported cooperative work conference, New York. New York: ACM. ACM Press New York, NY, USA.
  7. Montaner, M.; Lopez, B.; de la Rosa, J. L. (June 2003). "A Taxonomy of Recommender Agents on the Internet". Artificial Intelligence Review 19 (4): 285–330. doi:10.1023/A:1022850703159.
  8. Greg Linden, Brent Smith, and Jeremy York, “Amazon.com Recommendations” IEEE Computer Society, 1089-7801/03 Feb-2003.
  9. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86, (2002).
  10. D. Turney, “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews”, in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424, (2002).
  11. Hu and B. Liu, “Mining and summarizing customer reviews”, in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177,(2004).
  12. Pang and L. Lee, “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales”, in Proceedings of the 43rd Annual Meeting of the Association for Computation Linguistics, pp. 115–124, (2005).
  13. Terveen, W. Hill, B. Amento, D. McDonald, and J. Creter, “Phoaks: A system for sharing recommendations”, Communications of the ACM, 40(3), 59–62, (1997).
  14. Schelter, S., Bod en, C., et al., "Scalable similarity-based neighborhood methods with Map Reduce". Proc. 6th ACM conference on Recommender systems, Dublin, Ireland, pp. 163- 170,2012.
  15. Hadoop: Open source implementation of MapReduce,http : //lucene.apache.org/hadoop/.
  16. Lammel, R.: Google's MapReduce Programming ModelRevisited. Science of Computer Programming 70, 1-30, 2008.
  17. Zhao, W., Ma, H., et al.: Parallel K-Means Clustering Based on MapReduce. In: CloudCom 2009. LNCS, vol. 5931, pp. 674- 679,2009.
  18. Shvachko, H. Kuang, S. Radia, and R. Chans1er, "The Hadoop distributed file system," Mass Storage Systems and Technologies, IEEE/ NASA Goddard Conference, vol. 0, pp. 1- 10,2010.

Publication Details

Published in : Volume 2 | Issue 5 | September-October - 2016
Date of Publication Print ISSN Online ISSN
2016-10-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
155-159 IJSRSET162539   Technoscience Academy

Cite This Article

Yamini Nikam, M. B. Vaidya, "Service Recommendation Using Rating Inference Approach", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.155-159, September-October-2016.
URL : http://ijsrset.com/IJSRSET162539.php

IJSRSET Xplore

Subscribe

Conferences

National Conference on Advances in Mechanical Engineering 2017(NCAME 2017)

National Conference on Emerging Trends in Civil Engineering 2017( NCETCE 2017)