SPGProfile: Speak Group Profile

In this paper, we propose an XML-based recommender system, called SPGProfile. It is a type of collaborative information filtering system. SPGProfile uses ontology-driven social networks, where nodes represent social groups. A social group is an entity that defines a group based on demographic, ethnic, cultural, religious, age, or other characteristics. In the SPGProfile framework, query results are filtered and ranked based on the preferences of the social groups to which the user belongs. If the user belongs to social group G"x, results will be filtered based on the preferences of G"x and the preferences of each ancestor social group of G"x in the social network. SPGProfile can be used for various practical applications, such as Internet or other businesses that market preference-driven products. In the ontology, the preferences of a social group are identified from either: (1) the preferences of its member users or (2) from published studies about the social group. We describe and experimentally compare these two approaches. We also experimentally evaluate the search effectiveness and efficiency of SPGProfile and compare it to two existing search engines.

[1]  Geoffrey I. Webb,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Machine Learning for User Modeling , 1999 .

[2]  L. Stein,et al.  OWL Web Ontology Language - Reference , 2004 .

[3]  J. Logan.  Separate and Unequal: The Neighborhood Gap for Blacks and Hispanics in Metropolitan America. , 2002 .

[4]  Li Weihua.  Ontology supported intelligent information agent , 2002, Proceedings First International IEEE Symposium Intelligent Systems.

[5]  Fabio Gasparetti,et al.  Intelligent Search on the Internet , 2006, Reasoning, Action and Interaction in AI Theories and Systems.

[6]  Douglas W. Oard,et al.  The State of the Art in Text Filtering , 1997, User Modeling and User-Adapted Interaction.

[7]  Robert B. Allen,et al.  User Models: Theory, Method, and Practice , 1990, Int. J. Man Mach. Stud..

[8]  King-Lup Liu,et al.  Building efficient and effective metasearch engines , 2002, CSUR.

[9]  Carole M. Counihan,et al.  Good to Eat: Riddles of Food and Culture. Marvin Harris , 1987 .

[10]  Antonio Picariello,et al.  An intelligent search agent system for semantic information retrieval on the internet , 2003, WIDM '03.

[11]  Mukesh Dalal.  Personalized social & real-time collaborative search , 2007, WWW '07.

[12]  Clement T. Yu,et al.  Personalized Web search for improving retrieval effectiveness , 2004, IEEE Transactions on Knowledge and Data Engineering.

[13]  Epcd.  About - U.S. Census Bureau , 2012 .

[14]  Thomas Hofmann,et al.  Collaborative filtering via gaussian probabilistic latent semantic analysis , 2003, SIGIR.

[15]  Hsinchun Chen,et al.  A graph model for E-commerce recommender systems , 2004, J. Assoc. Inf. Sci. Technol..

[16]  Jennie Brand-Miller,et al.  Why Some Like it Hot: Food, Genes, and Cultural Diversity , 2005 .

[17]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[18]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[19]  M. Barker,et al.  The Cultural Feast: an Introduction to Food and Society , 2004 .

[20]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[21]  Michael McKernan,et al.  Everyone Eats: Understanding Food and Culture , 2008 .

[22]  Mark Levene,et al.  Personalisation of Web Search , 2003, ITWP.

[23]  Dimitrios Kalles,et al.  Algorithmic Aspects of Web Intelligent Systems , 2003 .

[24]  Ian Horrocks,et al.  OWL Web Ontology Language Reference-W3C Recommen-dation , 2004 .