A Combined Method for Mitigating Sparsity Problem in Tag Recommendation

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items' tag and existing tag ontology. We evaluated the approach on two public social book marking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods.

[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]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[3]  Pasquale Lops,et al.  Combining Collaborative and Content-Based Techniques for Tag Recommendation , 2010, EC-Web.

[4]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[5]  Asunción Gómez-Pérez,et al.  Review of the state of the art: discovering and associating semantics to tags in folksonomies , 2012, The Knowledge Engineering Review.

[6]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[7]  Antonina Dattolo,et al.  Recommending New Tags Using Domain-Ontologies , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[8]  Andreas Hotho,et al.  Information Retrieval in Folksonomies: Search and Ranking , 2006, ESWC.

[9]  Panagiotis Symeonidis,et al.  Tag recommendations based on tensor dimensionality reduction , 2008, RecSys '08.

[10]  Lars Schmidt-Thieme,et al.  Pairwise interaction tensor factorization for personalized tag recommendation , 2010, WSDM '10.

[11]  Bernardo A. Huberman,et al.  Usage patterns of collaborative tagging systems , 2006, J. Inf. Sci..

[12]  Andreas Hotho,et al.  Challenges in Tag Recommendations for Collaborative Tagging Systems , 2012, Recommender Systems for the Social Web.

[13]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[14]  Lars Schmidt-Thieme,et al.  Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems , 2010, PAKDD.

[15]  Andreas Hotho,et al.  Tag recommendations in social bookmarking systems , 2008, AI Commun..

[16]  Mor Naaman,et al.  HT06, tagging paper, taxonomy, Flickr, academic article, to read , 2006, HYPERTEXT '06.

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

[18]  Aldo Gangemi,et al.  Ontology Learning and Its Application to Automated Terminology Translation , 2003, IEEE Intell. Syst..

[19]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[20]  C. Bauckhage,et al.  Analyzing Social Bookmarking Systems : A del . icio . us Cookbook , 2008 .

[21]  Lars Schmidt-Thieme,et al.  Collaborative Tag Recommendations , 2007, GfKl.

[22]  R GruberThomas.  A translation approach to portable ontology specifications , 1993 .

[23]  Peter Mika.  Ontologies Are Us: A Unified Model of Social Networks and Semantics , 2005, International Semantic Web Conference.

[24]  Yue Xu,et al.  Constructing tag ontology from folksonomy based on WordNet , 2011 .

[25]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[26]  Vladimir Batagelj,et al.  Generalized Cores , 2002, ArXiv.

[27]  Evangelos E. Milios,et al.  Tag Sources for Recommendation in Collaborative Tagging Systems , 2009, DC@PKDD/ECML.