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

ISBN-10: 354077484X

ISBN-13: 9783540774846

This e-book constitutes the completely refereed post-proceedings of the eighth overseas Workshop on Mining internet information, WEBKDD 2006, held in Philadelphia, PA, united states in August 2006 along with the twelfth ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2006.

The thirteen revised complete papers offered including an in depth preface went via rounds of reviewing and development and have been rigorously chosen for inclusion within the publication. the improved papers exhibit new applied sciences from parts like adaptive mining tools, move mining algorithms, suggestions for the Grid, specifically flat texts, records, photographs and streams, usability, e-commerce functions, personalization, and advice engines.

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Extra 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,

Sample text

Also, a t-test was done in each of the case to show that the results of the two experiments were statistically different. The t-test is a statistical test which computes the probability (p) that two groups of a single parameter are members of the same population. A small (p) value means that the two results are statistically different. 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.

Therefore, a first goal is to develop nearest-neighbor algorithms that combine good accuracy with the advantage of scalability that model-based algorithms present. Regarding nearest-neighbor algorithms, there exist two main approaches: (a) user-based (UB) CF, which forms neighborhoods based on similarity between users; and (b) item-based (IB) CF, which forms neighborhoods based on similarities between items. However, both UB and IB are one-sided approaches, in the sense that they examine similarities either only between users or only between items, respectively.

Therefore, such a user has to be included in more than one clusters. 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.

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