Evaluation criteria for measuring the performance of recommender systems

The explosive growth of social network with the help of e-commerce websites has made the issue of information retrieval increasingly challenging. Users may not have the time or knowledge to personally evaluate the options which are available on social network platform. Recommender systems present themselves as a practical answer to endless options available online. In this research paper, we give an insight into various types of filtering techniques associated with recommender systems. We also discuss the problems faced by these filtering techniques and evaluation criteria on the basis of which various algorithms made for recommendation purpose can be compared. Finally we propose a composite news recommendation system model.

[1]  Jinghua Huang,et al.  A Survey of E-Commerce Recommender Systems , 2007, 2007 International Conference on Service Systems and Service Management.

[2]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[3]  Liang He,et al.  Evaluating recommender systems , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).

[4]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[5]  Nuria Oliver,et al.  Data Mining Methods for Recommender Systems , 2015, Recommender Systems Handbook.

[6]  Mouzhi Ge,et al.  Beyond accuracy: evaluating recommender systems by coverage and serendipity , 2010, RecSys '10.

[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]  Thuy Ngoc Nguyen,et al.  Towards context-aware recommendations: Strategies for exploiting multi-criteria communities , 2013, 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing.

[9]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[10]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[11]  Bart P. Knijnenburg,et al.  Explaining the user experience of recommender systems , 2012, User Modeling and User-Adapted Interaction.

[12]  Alexander Felfernig,et al.  Toward the Next Generation of Recommender Systems: Applications and Research Challenges , 2013 .

[13]  Roi Blanco,et al.  Learning Relevance of Web Resources across Domains to Make Recommendations , 2013, 2013 12th International Conference on Machine Learning and Applications.

[14]  James Bennett,et al.  The Netflix Prize , 2007 .

[15]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

[16]  Brahim Ouhbi,et al.  A comparison study of some algorithms in Recommender Systems , 2012, 2012 Colloquium in Information Science and Technology.

[17]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[18]  Song Chen,et al.  Social Network Based Recommendation Systems: A Short Survey , 2013, 2013 International Conference on Social Computing.

[19]  Francisco Chiclana,et al.  A social network representation for Collaborative Filtering Recommender Systems , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[20]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.