Advances in Web Mining and Web Usage Analysis: 8th by Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava,

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

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

The thirteen revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been conscientiously chosen for inclusion within the publication. the improved papers convey new applied sciences from components like adaptive mining equipment, circulate mining algorithms, strategies for the Grid, specially flat texts, records, photos and streams, usability, e-commerce functions, personalization, and suggestion engines.

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

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32 K. Beemanapalli, R. Rangarajan, and J. Srivastava 1000 Sessions, 20 Clusters 1000 Sessions, 20 Clusters 45 40 35 30 SSM Hit Ratio H i t R a tio 40 35 30 25 20 15 10 5 0 LASM 25 SSM 20 LASM 15 10 3 5 5 10 0 Number of Recommendations 3 5 10 Number of Recommendations Fig. 8. Hit Ratio vs Number of Recommendations for 1000 sessions 20 clusters Table 4. 125186 Table 5. 039619 3000 Sessions, 10 Clusters 3000 Sessions, 10 Clusters 40 35 30 25 20 35 SSM 15 10 5 0 LASM Hit Ratio H i t R a ti o 30 25 SSM 20 LASM 15 10 5 3 5 Number of Recommendations 10 0 3 5 10 Number of Recommendations Fig.

From our experimental study (Section 5) we understood that MAE is able to characterize the accuracy of prediction, but is not indicative for the accuracy of recommendation. Since in real-world recommender systems the experience of users mainly depends on the accuracy of recommendation, MAE may not be the preferred measure. For this reason we focus on widely accepted metrics from information retrieval. For a test user that receives a top-N recommendation list, let R denote the number of relevant recommended items (the items of the top-N list that are rated higher than Pτ by the test user).

Ru,i − ru )2 ∀u∈Ui (2) (ru,j − r u )2 ∀u∈Uj Neighborhood size: The number, k, of nearest neighbors used for the neighborhood formation is important, because it can affect substantially the system’s accuracy. In most related works [8,22], k has been examined in the range of values between 10 and 100. , sparsity). Therefore, CF algorithms should be evaluated against varying k, in order to tune it. Nearest-Biclusters Collaborative Filtering with Constant Values 41 Positive rating threshold: Recommendation for a test user is performed by generating the top-N list of items that appear most frequently in his formed neighborhood (this method is denoted as Most-Frequent item-recommendation).

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