Web Mining: From Web to Semantic Web

In this paper we look at the deployment of web usage mining results within two key application areas of web measurement and knowledge generation for personalisation. We take a fresh look at the model of interaction between business and visitors to their web sites and the sources of data generated during these interactions. We then look at previous attempts at measuring the effectiveness of the web as a channel to customers and describe our approach, based on scenario development and measurement to gain insights into customer behaviour. We then present Concerto, a platform for deploying knowledge on customer behaviour with the aim of providing a more personalized service. We also look at approaches to measuring the effectiveness of the personalization. Various standards that are emerging in the market that can ease the integration effort of personalization and similar knowledge deployment engines within the existing IT infrastructure of an organization are also presented. Finally, current challenges in the deployment of web usage mining are presented.

[1]  Lars Schmidt-Thieme,et al.  Mining Web Navigation Path Fragments , 2002 .

[2]  Georgios Paliouras,et al.  Web Usage Mining as a Tool for Personalization: A Survey , 2003, User Modeling and User-Adapted Interaction.

[3]  Andrew E. Fano,et al.  Building Recommender Systems using a Knowledge Base of Product Semantics , 2002 .

[4]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

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

[6]  Philip S. Yu,et al.  A new method for similarity indexing of market basket data , 1999, SIGMOD '99.

[7]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[8]  Tao Luo,et al.  Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization , 2004, Data Mining and Knowledge Discovery.

[9]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[10]  Pattie Maes,et al.  Design and implementation of an agent-based intermediary infrastructure for electronic markets , 2000, EC '00.

[11]  Jonathan L. Herlocker,et al.  Clustering items for collaborative filtering , 1999 .

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

[13]  Tom M. Mitchell,et al.  Learning to construct knowledge bases from the World Wide Web , 2000, Artif. Intell..

[14]  Bettina Berendt,et al.  Web-Usage-Based Success Metrics for Multi-Channel Businesses , 2003 .

[15]  Susan T. Dumais,et al.  Using Linear Algebra for Intelligent Information Retrieval , 1995, SIAM Rev..

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

[17]  Ian Horrocks DAML+OIL: A Reason-able Web Ontology Language , 2002, EDBT.

[18]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[19]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender Systems , 2000 .

[20]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[21]  Steffen Staab,et al.  Ontology-based text clustering , 2001, IJCAI 2001.

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

[23]  Andreas Hotho,et al.  Towards Semantic Web Mining , 2002, SEMWEB.

[24]  Prem Melville and Raymond J. Mooney and Ramadass Nagarajan Content-Boosted Collaborative Filtering , 2001 .

[25]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

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

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