By Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu
This publication constitutes the completely refereed post-proceedings of the seventh overseas Workshop on Mining internet information, WEBKDD 2005, held in Chicago, IL, united states in August 2005 together with the eleventh ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2005. The 9 revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been rigorously chosen for inclusion within the book.
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Additional resources for Advances in Web Mining and Web Usage Analysis: 7th International Workshop on Knowledge Discovery on the Web, WEBKDD 2005, Chicago, IL, USA, August 21,
2004). Mining fragments with fuzzy chains in molecular databases. Proc. Worksh. 49–60). 22. , & Esposito, R. (2004). Integrating web conceptual modeling and web usage mining. In Proc. WebKDD’04 Workshop at SIGKDD’04 (pp. 105–115). 23. Nijssen, S. N. (2004). A quickstart in frequent structure mining can make a diﬀerence. In Proc. SIGKDD’04 (pp. , Leiden, The Netherlands, Tech. nl. 24. , & Gonzalez, J. (2003). Conceptual user tracking. In Proc. AWIC’03 (pp. 155–164). 25. Srikant, R. & Agrawal, R.
Currently, most ontologies are hand crafted by experts as no fully automated tools are yet available. However, the clustering community has promising result with non-supervised incremental hierarchical conceptual algorithm such as COBWEB . For our experiments, we built a movie recommender system and the ontology modeling a movie. The concept tree was built from scratch based on our common sense as there is none modeling the movie domain.
Privacy. In most systems, the similarity matrix is located on a server and is accessible to a third party, thus raising privacy concerns. − Shilling attacks: malicious users can alter user ratings in order to influence the recommendations. The other widely used technique is preference-based recommendation. Here, a user is asked to express explicit preferences for certain attributes of the product. If preferences are accurately stated, multi-attribute utility theory (MAUT, 10) provides methods to find the most preferred product even when the set of alternatives is extremely large and/or volatile, and thus has no problems of sparsity, cold starts, latency or scalability.