Solving cold start problem in tag-based recommender systems using discrete imperialist competitive algorithm

Recommender systems detect users' favorites based on their past behavior and provide them with proper suggestions; however, these systems would encounter problems while dealing with users with low or empty usage data. This issue leads to the most prominent challenge of such systems called cold start. In thispaper, we proposea system based on which a modified discrete imperialist competitive algorithm where tags are clustered using K-medoids algorithm. When a new user logs in and enters his/her tags then the system will suggest just a few sources with the largest weight. Experimental results demonstrate improvement of evaluation criteria for recommender system in comparison with other methods.

[1]  Sean M. McNee,et al.  Getting to know you: learning new user preferences in recommender systems , 2002, IUI '02.

[2]  Anh Duc Duong,et al.  Addressing cold-start problem in recommendation systems , 2008, ICUIMC '08.

[3]  David Sánchez,et al.  Ontology-based information content computation , 2011, Knowl. Based Syst..

[4]  Pei-Min Chen,et al.  An information retrieval system based on a user profile , 2000, J. Syst. Softw..

[5]  Hyunbo Cho,et al.  An iterative semi-explicit rating method for building collaborative recommender systems , 2009, Expert Syst. Appl..

[6]  Daniel Thalmann,et al.  Merging trust in collaborative filtering to alleviate data sparsity and cold start , 2014, Knowl. Based Syst..

[7]  Yi-Cheng Ku,et al.  A semantic-expansion approach to personalized knowledge recommendation , 2008, Decis. Support Syst..

[8]  Stathes Hadjiefthymiades,et al.  Facing the cold start problem in recommender systems , 2014, Expert Syst. Appl..

[9]  Huan Liu,et al.  Social recommendation: a review , 2013, Social Network Analysis and Mining.

[10]  Michael J. Shaw,et al.  A preference scoring technique for personalized advertisements on Internet storefronts , 2006, Math. Comput. Model..

[11]  Yoon Ho Cho,et al.  Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations , 2010, Inf. Sci..

[12]  Ahmet Arslan,et al.  A collaborative filtering method based on artificial immune network , 2009, Expert Syst. Appl..

[13]  Duen-Ren Liu,et al.  Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands , 2008, Expert Syst. Appl..

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

[15]  王珊,et al.  Personalized Service System Based on Hybrid Filtering for Digital Library , 2007 .

[16]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[17]  Licia Capra,et al.  A Scalable Tag-Based Recommender System for New Users of the Social Web , 2011, DEXA.

[18]  Andreas Hotho,et al.  Tag Recommendations in Folksonomies , 2007, LWA.

[19]  Chen Honghui,et al.  User-based Clustering with Top-N Recommendation on Cold-Start Problem , 2013, 2013 Third International Conference on Intelligent System Design and Engineering Applications.

[20]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[21]  Christian Wartena,et al.  Using Tag Co-occurrence for Recommendation , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[22]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.