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Fast Algorithms for Mining Association Rules in Large Databases
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Web usage mining: discovery and applications of usage patterns from Web data
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User Modeling and User-Adapted Interaction
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Experiments on query expansion for internet yellow page services using web log mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Webview selection from user access patterns
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On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
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Predicting WWW surfing using multiple evidence combination
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W4A '08 Proceedings of the 2008 international cross-disciplinary conference on Web accessibility (W4A)
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ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
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From Web to Social Web: Discovering and Deploying User and Content Profiles
Solution Architecture for Visitor Segmentation and Recommendation Generation in Real Time
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Web Intelligence and Agent Systems
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COWES: Web user clustering based on evolutionary web sessions
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The "who is it?" problem application for customizable web sites
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The adaptive web
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Recsplorer: recommendation algorithms based on precedence mining
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Empirical Analysis of the Impact of Recommender Systems on Sales
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COWES: clustering web users based on historical web sessions
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To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.