International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 8, No. 7, 8 Jul. 2016
Cover page and Table of Contents: PDF (size: 741KB)
Pre-Recommendation Clustering and Review Based Approach for Collaborative Filtering Based Movie Recommendation
Full Text (PDF, 741KB), PP.72-80
Views: 0 Downloads: 0
Author(s)
Index Terms
Recommendation Systems, Collaborative Filtering, Clustering, Accuracy, Review
Abstract
The recommendation is playing an essential part in our lives. Precise recommendations facilitate users to swiftly locate desirable items without being inundated by irrelevant information. In the last few years, the amount of customers, products and online information has raised speedily and results out into the huge data analysis problem for recommender systems. While handling and evaluating such large-scale data, usual service recommender systems regularly undergo scalability and inefficiency problems. Nowadays, in multimedia platform such as movie, music, games, the use of Recommender System is increased. Collaborative Filtering is a dominant filtering technique used by many RSs. CF utilizes the rating history of the user to find out "like minded" users and this set of like-minded user is then used to recommend the movies which are liked by these like-minded users but did not watch by the active user. Thus, in CF, to find out the "neighborhood" the rating history of a user is used, but the reason behind the rating is not considered at all. This will lead to inaccuracy in finding a neighborhood set and subsequently in recommendation also. To cope with these scalability and accuracy challenges, this paper proposes an innovative solution, Clustering and Review based Approach for Collaborative Filtering based Recommendation. This innovative approach is enacted with the two stages; in the first stage the clustering of the available movies for recommendation is clustered into the subclasses for further computation. In the succeeding stage, the methodology based on reviews is utilized for finding neighborhood set in User Based Collaborative Filtering.
Cite This Paper
Saudagar L. Jadhav, Manisha P. Mali, "Pre-Recommendation Clustering and Review Based Approach for Collaborative Filtering Based Movie Recommendation", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.7, pp.72-80, 2016. DOI:10.5815/ijitcs.2016.07.10
Reference
[1]Saudagar L. Jadhav, Prof. Mrs. M. P. Mali, “A Survey on Various Approaches Used for Collaborative Filtering Based Recommendation,” International Journal on Advanced Computer Theory and Engineering (IJACTE), Vol. 4, Issue-1, Pages 39-44, 2015.
[2]Atisha Sachan and Vineet Richariya, “A Survey on Recommender Systems based on Collaborative Filtering Technique,” International Journal of Innovations in Engineering and technology (IJIET), vol. 2 no. 2, pp. 8-14, April 2013.
[3]Reena Pagare and Shalmali A. Patil, “Study of Collaborative Filtering Recommendation Algorithm - Scalability Issue,” International Journal of Computer Applications, vol. 67 - no. 25, pp. 0975 8887, April 2013.
[4]Robin Burke, “Hybrid Recommender Systems: Survey and Experiments,” User Modeling and User-Adapted Interaction, vol. 12 no.4, pp. 331-370, November 2002.
[5]J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, July 2013.
[6]G. Adomavicius and A. Tuzhilin, “Toward The Next Generation of Recommender Systems: A Survey of The State-of-the-art and Possible Extensions,” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol.17 - no. 6, pp. 734749, June 2005.
[7]Shan XU and Junzo WATADA, “A Method for Hybrid Personalized Recommender based on Clustering of Fuzzy User Profiles,” 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), vol. -no., pp. 2171-2177, 6-11 July 2014.
[8]Mukta Kohar and Chhavi Rana, “Survey Paper on Recommendation System,” International Journal of Computer Science and Information Technologies (IJCSIT), vol. 3 no. 2, pp. 3460-3462, 2012.
[9]J. Wen and W. Zhou, “An Improved Item-based Collaborative Filtering Algorithm Based on Clustering Method,” Journal of Computational Information Systems, vol. 8-no. 20, pp. 571-578, 2012.
[10]A. Kohrs and B. Merialdo, “Clustering for Collaborative Filtering Applications,” In Proceedings of Computational Intelligence for Modelling, Control & Automation, IOS Press, 1999.
[11]S. Saint Jesudoss, “Scalable Collaborative Filtering Recommendations Using Divisive Hierarchical Clustering Approach,” International Journal of Advanced Research in IT and Engineering, vol. 2 - no. 8, pp. 9-21, August 2013.
[12]S. Gong, “A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering,” Journal of Software, vol. 5 - no. 7, pp. 745-752, 2010.
[13]Xue, Gui-Rong, Chenxi Lin, Qiang Yang, WenSi Xi, Hua-Jun Zeng, Yong Yu, and Zheng Chen, “Scalable Collaborative Filtering Using Clusterbased Smoothing,” In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 114-121, ACM, 2005.
[14]Ungar, H. Lyle, and Dean P. Foster, “Clustering Methods for Collaborative Filtering,” AAAI Workshop on Recommendation Systems, vol. 1, pp. 1- 16, 1998.
[15]Rong Hu, Wanchun Dou and Jianxun Liu, “ClubCF: A Clustering-Based Collaborative Filtering Approach for Big Data Application,” IEEE Transactions on Emerging Topics in Computing, vol.2 - no.3, pp.302-313, Sept. 2014.
[16]Hyung and Jun Ahn, “A new similarity measure for collaborative filtering to alleviate the new user cold- starting problem,” Information Sciences, vol. 178 - no. 1, pp. 37-51, 2008.
[17]Tsang-Hsiang Cheng, Hung-Chen Chen, Wen-Ben Lin and Yen-Hsien Lee, “Collaborative filtering with user interest evolution,” 2011.
[18]Feng Wang and Li Chen, “Recommendation based on mining product reviews preference similarity network,” In Proceedings of 6th workshop on Social Network Mining and Analysis, 2012 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.166, 2012.
[19]Asher Levi, Osnat Mokryn, Christophe Diot, and Nina Taft, “Finding a needle in a haystack of reviews: cold start context-based hotel recommender system,” In Proceedings of the sixth ACM conference on Recommender systems (RecSys ’12), pp.115-122, 2012.
[20]Maria Terzi, Maria-Angela Ferrario and Jon Whittle, “Free Text In User Reviews: Their Role In Recommender Systems,” In Proceedings of the 3rd ACM RecSys10 Workshop on Recommender Systems and the Social Web, pp. 45-48, October 2011.
[21]S. Meng, W. Dou, X. Zhang and J. Chen, “KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications,” IEEE Transactions on Parallel and Distributed Systems, vol.25-no.12, pp.3221-3231, Dec. 2014.