By Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, Brij Masand
This booklet constitutes the completely refereed post-proceedings of the eighth foreign Workshop on Mining internet facts, 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 provided including an in depth preface went via rounds of reviewing and development and have been rigorously chosen for inclusion within the booklet. the improved papers convey new applied sciences from parts like adaptive mining equipment, circulation mining algorithms, innovations for the Grid, in particular flat texts, records, images and streams, usability, e-commerce functions, personalization, and suggestion engines.
Read or Download Advances in Web Mining and Web Usage Analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 Philadelphia, USA, August 20, PDF
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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
Analyzing the performance of shortcutting algorithms offline as done in this paper means that we must ignore the possibility that visitors’ browsing trails would be different in the presence of shortcutting links. A deployed version would also need to keep track of how often the presented shortcuts are used, and retain the most utilized shortcuts rather than replace them with new shortcuts. Deploying a shortcutting algorithm requires determining exactly how shortcuts will be added to webpages as links.
Precision) is expected to decrease. ) In related work [13,24], N usually takes values between 10 and 50. Evaluation Metrics: Several metrics have been used for the evaluation of CF algorithms, for instance the Mean Absolute Error (MAE) or the Receiving Operating Characteristic (ROC) curve [9,10]. MAE represents the absolute differences between the real and the predicted values and is an extensively used metric. 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.
Analogous reasoning applies for the IB case. Thus, a second goal is to develop nearest-neighbor algorithms that will be able to consider the duality between users and items, and at the same time, to capture partial matching of preferences. Finally, the fact that a user usually has various different preferences, has to be taken into account for the process of assigning him to clusters. 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.