Incorporating Concept Hierarchies into Usage Mining Based Recommendations

Recent studies have shown that conceptual and structural characteristics of a website can play an important role in the quality of recommendations provided by a recommendation system. Resources like Google Directory, Yahoo! Directory and web-content management systems attempt to organize content conceptually. Most recommendation models are limited in their ability to use this domain knowledge. We propose a novel technique to incorporate the conceptual characteristics of a website into a usage-based recommendation model. We use a framework based on biological sequence alignment. Similarity scores play a crucial role in such a construction and we introduce a scoring system that is generated from the website's concept hierarchy. These scores fit seamlessly with other quantities used in similarity calculation like browsing order and time spent on a page. Additionally they demonstrate a simple, extensible system for assimilating more domain knowledge. We provide experimental results to illustrate the benefits of using concept hierarchy.

[1]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[2]  Roy Rada,et al.  Ranking documents with a thesaurus , 1989, JASIS.

[3]  John Yen,et al.  Advances in Web Mining and Web Usage Analysis, 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Philadelphia, PA, USA, August 20, 2006, Revised Papers , 2007, WebKDD.

[4]  Jaideep Srivastava,et al.  USER: User-Sensitive Expert Recommendations for Knowledge-Dense Environments , 2005, WEBKDD.

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

[6]  Georgios Paliouras,et al.  Exploiting Probabilistic Latent Information for the Construction of Community Web Directories , 2005, User Modeling.

[7]  Anupam,et al.  Mining Web Access Logs Using Relational Competitive Fuzzy Clustering , 1999 .

[8]  Jaideep Srivastava,et al.  WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles , 2003, Lecture Notes in Computer Science.

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

[10]  Paul Brna,et al.  User Modeling 2005, 10th International Conference, UM 2005, Edinburgh, Scotland, UK, July 24-29, 2005, Proceedings , 2005, User Modeling.

[11]  Myoung-Ho Kim,et al.  Information Retrieval Based on Conceptual Distance in is-a Hierarchies , 1993, J. Documentation.

[12]  Osmar R. Zaïane,et al.  Combining Usage, Content, and Structure Data to Improve Web Site Recommendation , 2004, EC-Web.

[13]  Ming Gu,et al.  Spectral min-max cut for graph partitioning and data clustering , 2001 .

[14]  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..

[15]  Jaideep Srivastava,et al.  Creating adaptive Web sites through usage-based clustering of URLs , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[16]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[17]  A Min Tjoa,et al.  E-Commerce and Web Technologies , 2002, Lecture Notes in Computer Science.

[18]  Tao Luo,et al.  Effective personalization based on association rule discovery from web usage data , 2001, WIDM '01.

[19]  Jun Hong,et al.  Using Markov models for web site link prediction , 2002, HYPERTEXT '02.

[20]  Tao Luo,et al.  Using sequential and non-sequential patterns in predictive Web usage mining tasks , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[21]  Bamshad Mobasher,et al.  A Hybrid Web Personalization Model Based on Site Connectivity , 2003 .

[22]  Bamshad Mobasher,et al.  Impact of Site Characteristics on Recommendation Models Based On Association Rules and Sequential Patterns , 2003 .

[23]  Robin Burke,et al.  Inferring User’s Information Context from User Profiles and Concept Hierarchies , 2004 .

[24]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[25]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[26]  Prasanna Desikan,et al.  USER (User Sensitive Expert Recommendation): What Non-Experts NEED to Know , 2005 .

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

[28]  Stéphane Bressan,et al.  Efficiency and Effectiveness of XML Tools and Techniques and Data Integration over the Web , 2003, Lecture Notes in Computer Science.

[29]  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.

[30]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[31]  David M. Pennock,et al.  REFEREE: An Open Framework for Practical Testing of Recommender Systems using ResearchIndex , 2002, VLDB.

[32]  Daniel A. Keim,et al.  On Knowledge Discovery and Data Mining , 1997 .