Download Advances in Web Mining and Web Usage Analysis: 8th by Justin Brickell, Inderjit S. Dhillon (auth.), Olfa Nasraoui, PDF

By Justin Brickell, Inderjit S. Dhillon (auth.), Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, Brij Masand (eds.)

This e-book comprises the postworkshop complaints with chosen revised papers from the eighth foreign workshop on wisdom discovery from the internet, WEBKDD 2006. The WEBKDD workshop sequence has taken position as a part of the ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining (KDD) because 1999. The self-discipline of information mining promises methodologies and instruments for the an- ysis of enormous info volumes and the extraction of understandable and non-trivial insights from them. internet mining, a far more youthful self-discipline, concentrates at the analysisofdata pertinentto the Web.Web mining tools areappliedonusage info and site content material; they attempt to enhance our knowing of the way the net is used, to augment usability and to advertise mutual delight among e-business venues and their capability shoppers. Inthelastfewyears,theinterestfortheWebasamediumforcommunication, interplay and enterprise has resulted in new demanding situations and to extensive, devoted research.Many ofthe infancy difficulties in internet mining were solvedby now, however the large power for brand new and more advantageous makes use of, in addition to misuses, of the net are resulting in new demanding situations. ThethemeoftheWebKDD2006workshopwas“KnowledgeDiscoveryonthe Web”, encompassing classes discovered over the last few years and new demanding situations for the years yet to come. whereas a number of the infancy difficulties of net research have beensolvedandproposedmethodologieshavereachedmaturity,therealityposes newchallenges:TheWebisevolvingconstantly;siteschangeanduserpreferences flow. And, such a lot of all, an internet site is greater than a see-and-click medium; it's a venue the place a person interacts with a website proprietor or with different clients, the place workforce habit is exhibited, groups are shaped and reviews are shared.

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Sample text

In the first approach, we can automate and verify the distances calculated against user logs which are different from the logs used for calculating the distance values. In the second approach, we can evaluate the results by distributing questionnaires to users. This is similar to the approach proposed in the original paper [18]. The idea is to randomly sample web pages from the website and segregate them into groups based on their context. We can then calculate the distances between each of these pages.

To overcome this issue and to make our program highly scalable and memory efficient, we have taken the following approach: Each page is given a unique page id (starting from 0) and the set of links on a web page is stored as a linked list. The head of each of the linked list is stored in a vector called PageDetail. Thus PageDetail[0] points to the head of the linked list which stores the set of links on page 0. Each node in the linked list for page p stores the PageId of the page q to which it is connected, C(q->p), Average Clicks distance, Usage Score and the Usage aware Average-Clicks distance between page p and page q.

The above procedure was repeated for 3000 training sessions as well. Incorporating Usage Information into Average-Clicks Algorithm 1000 Sessions, 10 Clusters 1000 Sessions, 10 Clusters 50 45 40 H i t R a ti o Hit Ratio 35 30 SSM 25 LASM 20 15 10 40 35 30 25 20 15 10 5 0 SSM LASM 5 3 0 3 5 31 5 10 Number of Recommendations 10 Number of Recommendations Fig. 6. Hit Ratio vs No. of Recommendations for 1000 sessions, 10 clusters Table 2. 006292 1000 Sessions, 15 Clusters 45 40 35 30 25 20 15 10 5 0 40 35 SSM LASM 30 Hit Ratio Hit Ratio 1000 Sessions, 15 Clusters 25 SSM 20 LASM 15 10 3 5 5 10 0 Number of Recommendations 3 5 10 Number of Recommendations Fig.

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