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

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

ISBN-10: 354077484X

ISBN-13: 9783540774846

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

The thirteen revised complete papers awarded including an in depth preface went via rounds of reviewing and development and have been conscientiously chosen for inclusion within the e-book. the improved papers exhibit new applied sciences from parts like adaptive mining tools, circulate mining algorithms, strategies for the Grid, specially flat texts, files, photographs and streams, usability, e-commerce purposes, personalization, and advice 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,

Example text

The following box-plots and graphs compare the two models based on the Hit Ratio. The performance of both the models was recorded when the number of required recommendations was set to 3, 5 and 10. Both the models were trained on 1000 sessions and the Clustered Sessions [3] are represented as ClickStreams into 10, 15 and 20 clusters. Once the models were trained, they were tested on a different set of user sessions. Each of these experiments was repeated 5 times (using a different set of training sessions) to check the consistency of the results.

Thus, we create the test users’ neighborhood, consisted of the k nearest biclusters. Then, we provide for each test user a Top-N recommendation list based on the most frequent items in his neighborhood. To ease the discussion, we will use the running example illustrated in Figure 1, where I1−7 are items and U1−9 are users. As shown, the example data set is divided into training and test set. The null cells (no rating) are presented with dash. U1 U2 U3 U4 U5 U6 U7 U8 I1 5 2 4 5 1 I2 3 2 1 2 4 I3 2 4 2 1 4 5 - I4 1 4 2 1 5 I5 1 4 2 5 4 4 I6 3 5 1 1 3 I7 5 2 3 - (a) I 1 I2 I3 I 4 I5 I6 I 7 U9 5 - 4 - 1 - 2 (b) Fig.

These bilcusters are summarized as follows: b1 : Ub1 = {U3 , U6 }, Ib1 = {I1 , I7 } b2 : Ub2 = {U5 , U7 , U2 }, Ib2 = {I5 , I3 } b3 : Ub3 = {U2 , U8 }, Ib3 = {I6 , I5 } b4 : Ub4 = {U8 , U4 }, Ib4 = {I4 , I2 , I6 } Nearest-Biclusters Collaborative Filtering with Constant Values 45 We have to notice that there is overlap between biclusters. Specifically, between biclusters 2 and 3 in item I5 . Also, we have overlapping between biclusters 3 and 4 in item I6 . We can allow this overlapping (it reaches 16,6%) or we can forbid it.

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Advances in Web Mining and Web Usage Analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 Philadelphia, USA, August 20, by Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, Brij Masand


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