New PDF release: Advances in Web Mining and Web Usage Analysis: 8th

By Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, Brij Masand

This publication constitutes the completely refereed post-proceedings of the eighth foreign Workshop on Mining net information, WEBKDD 2006, held in Philadelphia, PA, united states in August 2006 along side the twelfth ACM SIGKDD foreign convention on wisdom Discovery and information Mining, KDD 2006.

The thirteen revised complete papers provided including a close preface went via rounds of reviewing and development and have been rigorously chosen for inclusion within the e-book. the improved papers express new applied sciences from components like adaptive mining equipment, movement mining algorithms, concepts for the Grid, specially flat texts, records, images and streams, usability, e-commerce functions, personalization, and advice engines.

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Additional info for Advances in Web Mining and Web Usage Analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 Philadelphia, USA, August 20,

Example text

For a test session (p1, p2,…, pi…, pj…, pn), if pj is recommended at page pi, and pj is subsequently accessed in the session, then the click reduction due to this recommendation is, Click reduction = j −i i High hit ratio indicates good quality recommendations. 1 Comparison of Results In the following figures, we refer to the ‘2,-1’ model as Session Similarity Model (SSM) and our model as Link Aware Similarity Model (LASM). The following box-plots and graphs compare the two models based on the Hit Ratio.

The proposed approach is described in Section 4. Experimental results are given in Section 5. Finally, Section 6 concludes this paper. 2 Related Work In 1992, the Tapestry system [6] introduced Collaborative Filtering (CF). In 1994, the GroupLens system [21] implemented a CF algorithm based on common users preferences. Nowadays, it is known as user-based CF algorithm, because it employs users’ similarities for the formation of the neighborhood of nearest users. , [8,18,23]. In 2001, another CF algorithm was proposed.

Notice that this cannot be achieved by most of the traditional clustering algorithms, which place each item/user in exactly one cluster. In conclusion, a third goal is to adopt an approach that does not follow the aforementioned restriction and can cover the entire range of the user’s preferences. , to develop scalable nearest-neighbor algorithms, we propose the grouping of different users or items into a number of clusters, based on their rating patterns. This way, similar searching is performed efficiently, because we use consolidated information (that is, the clusters) and not individual users or items.

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