Feature Analysis of Recommender Techniques Employed in the Recommendation Engines

Problem statement: Recommender Systems (RS) have become a widely researched area as it is extensively used in web usage mining and E-co mmerce platforms. Approach: There were a number of recommender systems available to suggest the web pages for the web users. Results: A recommender system acted as an intelligent intermed iary that automatically generates and predicts information and web pages, which suit the users' be havior and users' needs. Conclusion: The various recommender models and analyzing the key features of those models and analyzing the features of portal sites that employ recommender systems to hel p the research community are the key features of this study and survey.

[1]  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.

[2]  Antonio Badia,et al.  A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites , 2008, IEEE Transactions on Knowledge and Data Engineering.

[3]  Russell Greiner,et al.  Using Learned Browsing Behavior Models to Recommend Relevant Web Pages , 2005, IJCAI.

[4]  Guy Shani,et al.  An MDP-Based Recommender System , 2002, J. Mach. Learn. Res..

[5]  M. Tamer Özsu,et al.  A Web page prediction model based on click-stream tree representation of user behavior , 2003, KDD '03.

[6]  Xiaoyuan Su,et al.  Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[7]  Taghi M. Khoshgoftaar,et al.  Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[8]  Stanley Loh,et al.  A Tourism Recommender System Based on Collaboration and Text Analysis , 2003, J. Inf. Technol. Tour..

[9]  Hans Hummel,et al.  Recommendations for learners are different: Applying memory-based recommender system techniques to lifelong learning , 2007 .

[10]  Hideyuki Kawashima,et al.  PORSCHE: A physical objects recommender system for cell phone users , 2006 .

[11]  Athena Vakali,et al.  Time-Aware Web Users' Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.

[12]  Pedro M. Domingos,et al.  Relational Markov models and their application to adaptive web navigation , 2002, KDD.

[13]  Don Monroe.  Just for you , 2009, Commun. ACM.

[14]  Nicholas Ampazis,et al.  Collaborative Filtering via Concept Decomposition on the Netflix Dataset , 2008 .

[15]  Alexandre de Spindler,et al.  Harnessing facebook for the evaluation of recommender systems based on physical copresence , 2008, ECAI 2008.

[16]  Choochart Haruechaiyasak,et al.  A data mining framework for building a Web-page recommender system , 2004, Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, 2004. IRI 2004..

[17]  Fabrizio Silvestri,et al.  A privacy preserving web recommender system , 2006, SAC.

[18]  R. Dieng-Kuntz,et al.  A Graph-Based Algorithm for Alignment of OWL Ontologies , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[19]  Dariusz Król,et al.  The Recommendation Mechanism in an Internet Information System with Time Impact Coefficient , 2006, Int. J. Comput. Sci. Appl..

[20]  Tomoharu Iwata,et al.  Recommendation Method for Improving Customer Lifetime Value , 2008, IEEE Transactions on Knowledge and Data Engineering.

[21]  Russell Greiner,et al.  An Effective Complete-Web Recommender System , 2003, WWW.

[22]  Hendrik Drachsler,et al.  Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model , 2008, Int. J. Learn. Technol..

[23]  A. Tuzhilin,et al.  Extending Recommender Systems : A Multidimensional Approach , 2001 .

[24]  Barry Smyth,et al.  Trust in recommender systems , 2005, IUI.

[25]  Jaideep Srivastava,et al.  Incorporating Concept Hierarchies into Usage Mining Based Recommendations , 2006, WEBKDD.