Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study

Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users' perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.

[1]  Guihua Nie,et al.  Exploiting Semantic Descriptions of Products and User Profiles for Recommender Systems , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

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

[3]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[4]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[5]  Jorge García Duque,et al.  ATLAS: a framework to provide multiuser and distributed t‐learning services over MHP , 2006, Softw. Pract. Exp..

[6]  Duco Das,et al.  Recommender Systems for TV Duco Das and Herman ter Horst , 1998 .

[7]  Alan Griffiths.  Digital Television Strategies: Business Challenges and Opportunities , 2002 .

[8]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[9]  Michael J. Pazzani,et al.  & Webert: Identifying i teresting web sites , 1996 .

[10]  Upendra Shardanand.  Social information filtering for music recommendation , 1994 .

[11]  Zhiwen Yu,et al.  Ontology-Based Semantic Recommendation for Context-Aware E-Learning , 2007, UIC.

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

[13]  Jorge García Duque,et al.  Avatar: Enhancing the Personalized Television by Semantic Inference , 2007, Int. J. Pattern Recognit. Artif. Intell..

[14]  Fan-Sheng Kong,et al.  Semantic-Enhanced Personalized Recommender System , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[15]  Stuart Edward Middleton,et al.  Capturing knowledge of user preferences with recommender systems , 2003 .

[16]  Jorge García Duque,et al.  A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems , 2008, Knowl. Based Syst..

[17]  Tim Beardsley.  Tool Time on Cactus Hill , 1998 .

[18]  Piet Kommers,et al.  iTV Enhanced System for Generating Multi-Device Personalized Online Learning Environments , 2004 .

[19]  John Zimmerman,et al.  Chapter 5 TV PERSONALIZATION SYSTEM Design of a TV Show Recommender Engine and Interface , 2003 .

[20]  John Zimmerman,et al.  Content Augmentation Aspects of Personalized Entertainment Experience , 2003 .

[21]  Lars Schmidt-Thieme,et al.  Exploiting Semantic Product Descriptions for Recommender Systems , 2004 .

[22]  Ian Horrocks,et al.  OIL: An Ontology Infrastructure for the Semantic Web , 2001, IEEE Intell. Syst..

[23]  Dan Brickley,et al.  Rdf vocabulary description language 1.0 : Rdf schema , 2004 .

[24]  I-Ching Hsu,et al.  SXRS: An XLink-based Recommender System using Semantic Web technologies , 2009, Expert Syst. Appl..

[25]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[26]  Ted Briscoe,et al.  32nd Annual Meeting of the Association for Computational Linguistics, 27-30 June 1994, New Mexico State University, Las Cruces, New Mexico, USA, Proceedings , 1994, ACL.

[27]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[28]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[29]  Duco A. D. Das,et al.  Recommender Systems for TV , 1998 .

[30]  Bradley N. Miller,et al.  MovieLens unplugged: experiences with an occasionally connected recommender system , 2003, IUI '03.

[31]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[32]  Jennifer Widom,et al.  Exploiting hierarchical domain structure to compute similarity , 2003, TOIS.

[33]  Alfred Kobsa,et al.  Personalised hypermedia presentation techniques for improving online customer relationships , 2001, The Knowledge Engineering Review.

[34]  Alfred Kobsa,et al.  Personalized Digital Television: Targeting Programs to Individual Viewers (Human-Computer Interaction Series, 6) , 2004 .

[35]  James A. Hendler,et al.  A new form of Web content that is meaningful to computers will unleash a revolution of new possibili , 2002 .

[36]  Gary Boone,et al.  Concept features in Re:Agent, an intelligent Email agent , 1998, AGENTS '98.

[37]  Roy Rada,et al.  Development and application of a metric on semantic nets , 1989, IEEE Trans. Syst. Man Cybern..

[38]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[39]  Deborah L. McGuinness,et al.  OWL Web ontology language overview , 2004 .

[40]  Alexander Tuzhilin,et al.  Towards the Next Generation of Recommender Systems , 2010, ICE-B 2010.