Advances in Web Mining and Web Usage Analysis: 6th by Bamshad Mobasher, Olfa Nasraoui, Bing Liu, Brij Masand

By Bamshad Mobasher, Olfa Nasraoui, Bing Liu, Brij Masand

This ebook constitutes the completely refereed post-proceedings of the sixth foreign Workshop on Mining internet info, WEBKDD 2004, held in Seattle, WA, united states in August 2004 along with the tenth ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2004.

The eleven revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been carfully chosen for inclusion within the e-book. The prolonged papers are subdivided into four basic teams: net utilization research and person modeling, internet personalization and recommender structures, seek personalization, and semantic internet mining. The latter comprises additionally papers from the joint KDD workshop on Mining for and from the Semantic internet, MSW 2004.

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Extra info for Advances in Web Mining and Web Usage Analysis: 6th International Workshop on Knowledge Discovery on the Web, WEBKDD 2004, Seattle, WA, USA, August 22-25,

Example text

Am ) := μ(A1 ) ∪ {∞} × · · · × μ(A1 ) ∪ {∞} . ,Am : D(A1 , . . , Am ) → μ(A1 , . . ,Am (a) = μA1 (a1 ), . . , μAm (am ) , a := (a1 , . . , am ). ,Am ) denotes a mining vector with coordinates a ∈ D(A1 , . . ,Am . Subsequently, we refer to a mining vector as vector only. A sequence (that is an ordered multi-set) A := (a1 , . . , an ) , n ∈ N is called data matrix. If there exists a total order μ(A ), , ∈ {1, . . , m} and if the rows of A are ordered ascending or descending according to μ(A ), , A is called ordered data matrix.

In the on-line phase, a recommendation set is generated by matching the current active session and all usage profiles. Similarly, another recommendation set is generated by matching the current active session and all content profiles. Finally, a set of pages with the maximum recommendation value across the two recommendation sets is presented as recommendation. This is called a weighted hybridization method [3]. In [16], Nakagawa and Mobasher use association rule mining, sequential pattern mining, and contiguous sequential mining to generate three types of navigational patterns in the off-line phase.

Moreover, this generalizes our notion of mission. In addition, this highlights the importance to have application related logs rather than just relying on information poor web server logs. com) is a commercial system developed by the Centre of Health Evidence at the University of Alberta as a gate to a multitude of applications and on-line resources, and is used by hospital personnel and other health practitioners. It has its specific session-based activity log which records details about user accesses to on-line pages via different applications.

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