UPCAR: User Profile Clustering based Approach for Recommendation

Providing users with the most convenient information content is the challenge of personalized information systems. Basing on the concept of the user profile, personalization can show up either by adapting the interface, filtering information or proposing recommended content to the user. In this work, we propose a user profile clustering based approach to personalize information systems by offering recommendations. Our approach is based on a proposed user profile similarity function measure. Experimental evaluation in an e-commerce context has been performed to validate our clustering approach.

[1]  Lingling Meng,et al.  A Review of Semantic Similarity Measures in WordNet 1 , 2013 .

[2]  Alfred Kobsa,et al.  Generic User Modeling Systems , 2001, User Modeling and User-Adapted Interaction.

[3]  Ali Idri,et al.  User profile model: A user dimension based classification , 2015, 2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA).

[4]  Yoon Ho Cho,et al.  A personalized recommender system based on web usage mining and decision tree induction , 2002, Expert Syst. Appl..

[5]  Alfred Kobsa,et al.  Generic User Modeling Systems , 2001, User modeling and user-adapted interaction.

[6]  David McLean,et al.  An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources , 2003, IEEE Trans. Knowl. Data Eng..

[7]  Alessandro Micarelli,et al.  User Profiles for Personalized Information Access , 2007, The Adaptive Web.

[8]  Florence Sèdes,et al.  A personalized recommendation framework based on cam and document annotations , 2010, RecSysTEL@RecSys.

[9]  F. O. Isinkaye,et al.  Recommendation systems: Principles, methods and evaluation , 2015 .

[10]  Omar El Beqqali,et al.  User profile Ontology for the Personalization approach , 2012 .

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

[12]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[13]  Luis M. de Campos,et al.  Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks , 2010, Int. J. Approx. Reason..

[14]  Keinosuke Matsumoto,et al.  A Supporting System for Cloud Service Integration Based on User Profiles , 2013 .

[15]  Michal Kompan,et al.  User Preference Modeling by Global and Individual Weights for Personalized Recommendation , 2015 .

[16]  Mohand Boughanem,et al.  Inferring the user interests using the search history , 2006, LWA.

[17]  Richard Chbeir,et al.  User Profile Matching in Social Networks , 2010, 2010 13th International Conference on Network-Based Information Systems.

[18]  Hongming Cai,et al.  A Hybrid User Profile Model for Personalized Recommender System with Linked Open Data , 2014, 2014 Enterprise Systems Conference.