Advances in Web Mining and Web Usage Analysis: 7th by Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad

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By Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu

This e-book constitutes the completely refereed post-proceedings of the seventh overseas Workshop on Mining internet facts, WEBKDD 2005, held in Chicago, IL, united states in August 2005 at the side of the eleventh ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2005. The 9 revised complete papers awarded including a close preface went via rounds of reviewing and development and have been conscientiously chosen for inclusion within the book.

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Extra info 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,

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In [24] used the depth of the lowest common ancestor. In [16], Resnik defined similarity based on the information content shared by the concepts rather than its distance. Resnik's metric postulates higher similarity among rare concepts. While this makes sense for concepts themselves, there is no reason why this would also hold for preferences. K Car Mercedes Boat Depth= L Vehicle Fig. 1. : the similarity between concepts A and B is identical to the similarity between concepts B and A. However, in real life situations, the distance should be considered asymmetric.

R. & Han, J. (1998). Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In Proc. ADL’98 (pp. 19–29). 31. J. (2002). Efficiently mining trees in a forest. In Proc. SIGKDD’02 (pp. 71–80). ch Abstract. To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction models.

This is commonly called the Weighted Additive Strategy (WADD, [10]), and the item with then highest overall evaluation value is chosen as the optimal solution. n V (ok ) = ∑ wi vi (ok ) (1) i =1 Theoretically, when the MPI assumption holds, this strategy can achieve 100% accuracy if all the parameters can be precisely elicited. Unfortunately, the elicitation of the parameters is expensive and various authors have tried to simplify this elicitation process in order to make it usable in real systems.

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