Solving Sparsity Problem in Movie Based Recommendation System

  • Pratibha Bargah Rungta college of engineering and technology bhilai
  • Nitin Mishra Rungta collage engineering & Technology Bhilai (c.g), India
Keywords: k-mean clustering, euclidean distance, k-mediod clustering, Harmonic Mean.

Abstract

Movie Recommendation is more useful in our community life due to its strength in giving enhanced entertainment. Recommendation system can advise a collection of movies to users depend on their choice, or the popularities of the movies. while, a set of motion picture recommendation systems have been planned, mainly of these either cannot advise a movie to the presented users powerfully.

In this paper we propose a solve the sparsity problem in movie recommendation system that has the ability to recommend movies to a new user as well as the others. It mines movie databases to collect all the important information, such as, popularity and attractiveness, required for recommendation. But in Recommendation system has many problems like sparsity , cold start , first Rater problem , Unusual user problem. K- mean clustering  is the most successful method of Recommender System. K- means clustering also K-Means Clustering. The Algorithm K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori.

References

(1) Yibo chen et al progress of solving sparsity problem in recommendation system using association retrival .progress journal of computers vol 6 9september year:2011.

(2) Lalita sharma, anju gera et al progress of hybird approaches to reduce the sparsity problem . Progress on Mtech. Scholar BSAITM faridabad. vol 6 _july 2013.

(3) yu rong ,xiao wen, hong cheng, et al an monte carlo algorithm for cold start problem International world wide web conference committee April 7-11-2014.

(4) Mohammed mahmuda rahumen rahumen lecture, et al contextual recommendation system using multidimensional approch. International journal of intelligent information system august20,2013.

(5) Zuping liu sichuon et al recommendation algorithm based on user intrest ,advanced science and technology letters vol. 53, 2014.

(6) Manos papagelis, dimitris plexousakis Alleviating the sparsity problems of collaborative filtering using trust inferences Institutes of computer science , foundation for research and technology- hellas Years:2004.

(7) Andy yuanxue, jianzhong Qi , Solving the data sparsity problem in destination prediction University of Melbourne , Australia Year: 2013.

(8) Beau piccart, jan struf Alleviating the sparsity problem in collaborative filtering by using an adapted distance and a graph based method. IEEE computer technology Year:2007.

(9) Badrul sarwar, george karypis , joseph konstan Item based collaborative filtering recommedation algorithm . Department of computer science and engineering, University of Minnesota Year:2006 .

(10) Badrul sarwar, ,joseph konstan john riedl Using filtering agent to improve prediction quality in the gruoplen research collaborative filtering Department of computer science and engineering , University of minnesota year:2008 .

(11) Chrsistian Desrosiers, George Karypis. Solving the Sparsity Problem: Collaborative Filtering via Indirect Similarities. Technical Report. Department of Computer Science and Engineering University of Minnesota 4-192 EECS Building 200 Union Street SE Minneapolis, MN 55455-0159 USA. 2008.

(12) Zan Huang, Hsinchun Chen, et al. Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering. ACM Transactions on Information Systems, Vol. 22, No. 1, January 2004,

–142. http://dx.doi.org/10.1145/963770.963775.

(13) Sanghack Lee and Jihoon Yang and Sung-Yong Park, Discovery of

Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem, Discovery Science, 2007.

(14) Rong Jin, Luo Si, et al. Collaborative Filtering with Decoupled Models for Preferences and Ratings. CIKM ’03, New Orleans, Louisiana, USA, November 3-8, 2003.

Liu Jianguo, Zhou Tao, et al. Overview of the Evaluated Algorithms for the Personal Recommendation Systems. Complex System and Complexity Science. 2009, Vol.6, No.3, 1-10

Published
2016-10-24