Movies recommendation system using collaborative filtering and k-means

The purpose of this research is to develop a movie recommender system using collaborative filtering technique and Kmeans. Collaborative filtering is the most successful algorithm in the recommender system’s field. A recommender system is an intelligent system that can help a user to come across interesting items. This paper considers the users m (m is the number of users), points in n dimensional space (n is the number of items) and we present an approach based on user clustering to produce a recommendation for the active user by a new approach. We used k-means clustering algorithm to categorize users based on their interests. We evaluate the traditional collaborative filtering and our approach to compare them. Our results show the proposed algorithm is more accurate than the traditional existing one, besides it is less time consuming than the previous existing methods.

[1]  Lars Schmidt-Thieme,et al.  Guest Editors' Introduction: Recommender Systems , 2007, IEEE Intell. Syst..

[2]  Songjie Gong A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering , 2010, J. Softw..

[3]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[4]  Tsan-sheng Hsu,et al.  Privacy-Preserving Collaborative Recommender Systems , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Rong Hu,et al.  Acceptance issues of personality-based recommender systems , 2009, RecSys '09.

[6]  Gediminas Adomavicius,et al.  Recommendation Technologies: Survey of Current Methods and Possible Extensions , 2003 .

[7]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  Urszula Kuzelewska Advantages of Information Granulation in Clustering Algorithms , 2011, ICAART.

[9]  Ram D. Gopal,et al.  Empirical Analysis of the Impact of Recommender Systems on Sales , 2010, J. Manag. Inf. Syst..

[10]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[11]  Yanchang Zhao R and Data Mining: Examples and Case Studies , 2012 .

[12]  David McSherry,et al.  Explaining the Pros and Cons of Conclusions in CBR , 2004, ECCBR.

[13]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[14]  N. Nalini,et al.  Recommender system in ubiquitous commerce , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[15]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .