A Hybrid Approach to Solve Cold Start Problem in Recommender Systems using Association Rules and Clustering Technique

purposes is increasing at an astonishing rate. More and more people rely on online sites for purchasing songs, apparels, books, rented movies etc. The competition between the online sites forced the web site owners to provide personalized services to their customers. So the recommender systems came into existence. Recommender systems are active information filtering systems that attempt to present to the user, information items in which the user is interested in. The websites implement recommender system feature using collaborative filtering, content based or hybrid approaches. The recommender systems also suffer from issues like cold start, sparsity and over specialization. Cold start problem is that the recommenders cannot draw inferences for users or items for which it does not have sufficient information. This paper attempts to propose a solution to the cold start problem by combining association rules and clustering technique. Comparison is done between the performance of the recommender system when association rule technique is used and the performance when association rule and clustering is combined. The experiments with the implemented system proved that accuracy can be improved when association rules and clustering is combined. An accuracy improvement of 36% was achieved by using the combination technique over the association rule technique.

[2]  Harith Alani,et al.  Exploiting Synergy Between Ontologies and Recommender Systems , 2002, Semantic Web Workshop.

[3]  John Riedl,et al.  Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering , 2002 .

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

[5]  Gerd Stumme,et al.  Generating a Condensed Representation for Association Rules , 2005, Journal of Intelligent Information Systems.

[6]  H. Sobhanam,et al.  Addressing cold start problem in recommender systems using association rules and clustering technique , 2013, 2013 International Conference on Computer Communication and Informatics.

[7]  Byeong Man Kim,et al.  Clustering approach for hybrid recommender system , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[8]  Yue Xu,et al.  Using Association Rules to Solve the Cold-Start Problem in Recommender Systems , 2010, PAKDD.

[9]  Korris Fu-Lai Chung,et al.  Applying Cross-Level Association Rule Mining to Cold-Start Recommendations , 2007, 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops.

[10]  Yue Xu,et al.  Extracting Non-redundant Approximate Rules from Multi-level Datasets , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[11]  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.

[12]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[13]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[14]  Liu Xin.  A recommendation system combining user clustering and association rules with multiple minimum support , 2012 .

[15]  Sunita B Aher,et al.  Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning , 2012 .

[16]  Byeong Man Kim,et al.  An approach for combining content-based and collaborative filters , 2003, IRAL.

[17]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[18]  John Riedl,et al.  Learning preferences of new users in recommender systems: an information theoretic approach , 2008, SKDD.

[19]  Harald Steck,et al.  Training and testing of recommender systems on data missing not at random , 2010, KDD.