Articles

Semantic Based Service Recommendation Using Collaborative Filter With Opinion Mining

Semantic Based Service Recommendation Using Collaborative Filter With Opinion Mining

  IJPTT-book-cover
 		 International Journal of P2P Network Trends and Technology (IJPTT)          
 
© 2016 by IJPTT Journal
Volume - 6 Issue - 6
Year of Publication : 2016
Authors : G. M. Ramkumar, T. Vijaya Saratha, K. K. Kavitha

Citation

G. M. Ramkumar , T. Vijaya Saratha, K. K. Kavitha "Semantic Based Service Recommendation Using Collaborative Filter With Opinion Mining". International Journal of P2P Network Trends and Technology (IJPTT), V6(6):1-6 Nov - Dec 2016, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.

Abstract

Recommendation system acts as a tool in providing most appropriate service to the user. Currently, information through online services increases. This leads to the overhead of data in online and there is a possibility of getting less accurate results. In previous approaches, recommendation of service is based on the feedbacks and ranking from the previous user. It doesn’t consider the suggestion of the user at a time, who in need of searching for the particular service. The proposed system deals with the implementation of personalized recommendation to provide services for hotel reservation system. Preferences are collected from the active user about particular service for each application. Similar user’s opinions are taken from the reviews using keyword extraction method and Supervised learning algorithms are used to identify sentiment orientation. It determines positive or negative opinion along with negation word near to each opinion word and then identifies the number of positive and negative opinions of reviews. Keywords with positive opinion are considered and similarity is calculated between user preferences with reviews of the previous user by jaccord and cosine measures. From this most similar keywords are provided to the user as recommended service. To provide more accurate prediction of the services needed by the active user the proposed system is implemented using MapReduce framework.

References

[1] J.Manyika et al. “Big data: The next frontier for innovation, competition, and productivity,”McKinsey& Company Publications, 2011.
[2] C. Lynch, “Big Data: How Do Your Data Grow?,” CNI Publication, vol. 455, no. 7209, pp. 28-29, 2008.
[3] Watkins, Andrew B, “Exploiting immunological metaphors in the development of serial, parallel, and distributed learning algorithms”, Diss. University of Kent at Canterbury, 2005.
[4] Liu, Bing ,“Opinion mining and sentiment analysis,” Proc. Springer Berlin Heidelberg, vol.2, pp. 459-526, Jan. 2011.
[5]Zhao, Zhi-Dan, and Ming-Sheng Shang, "User-based collaborative-filtering recommendation algorithms on hadoop," Proc. IEEE 3rd International Conference onKnowledge Discovery and Data Mining, vol. , pp. 478-481,Jan. 2010.
[6]G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, IEEE Trans.Internet Computing, vol. 7, no. 1, pp. 76-80, Jan. 2003.
[7] M. Bjelica, “Towards TV Recommender System Experiments with User Modeling,” IEEE Trans. Consumer Electronics, vol. 56, no. 3,pp. 1763-1769, Aug. 2010.
[8] M. Alduan, F. Alvarez, J. Menendez, and O. Baez, “Recommender System for Sport Videos Based on User Audiovisual Consumption,” IEEE Trans. Multimedia, vol. 14, no. 6, pp. 1546-1557, Dec. 2012.
[9]Sikka R, Dhankhar A, Rana C.,“A survey paper on e-learning recommender system,” International Journal of Computer Applications, vol. 47,no. 9, pp. 27-30, Jun. 2012 .
10]Lam, Chuck, “Hadoop in action,”Manning Publications Co., 2010.
[11] Ghemawat, Sanjay, Howard Gobioff, Shun-Tak Leung, “The Google file system,” In ACM SIGOPS Operating Systems Review, vol. 37, No. 5, pp. 29-43,2003.
[12] Turney and Peter D.,“semantic orientation applied to unsupervised classification of reviews,” In Proceedings of the 40th annual meeting on association for computational linguistics, pp. 417-424,2002.
[13]Meng, S., Dou, W., Zhang, X., & Chen, J., “ KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications” , IEEE Trans. Parallel and Distributed Systems, vol.25, no.12, pp. 3221-3231, Dec. 2014.
[14]Singam, J. Amaithi, and S. Srinivasan, “optimal keyword search for recommender system in big data application,”ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 7, April 2006.
[15] Turney and Peter D , “semantic orientation applied to unsupervised classification of reviews”, “ Proc. of the 40th annual meeting on association for computational linguistics, pp. 417-424, 2002.
[16] Zhang L., Liu B., Lim S. H., & O`Brien-Strain E., “ Extracting and ranking product features in opinion documents,” Proc. of the 23rd International Conference on Computational Linguistics: Posters () Association for Computational Linguistics, pp. 1462-1470, Aug. 2010.
[17] Hu, Minqing, and Bing Liu, “Mining opinion features in customer reviews,” AAAI ,vol. 4. no. 4, 2004.
[18]http://www.tripadvisor.com.
[19]http://www.caranddriver.com.
[20]http://www.imdb.com.
[21]http://www.yelp.com.
[22]http://www.biomedcentral.com.

Keywords
Keyword, Preferences, Recommender system, Hadoop, MapReduce.