Algorithms for clustering data
Algorithms for clustering data
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Working the Web: An Empirical Model of Web Use
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Learning a model of a web user's interests
UM'03 Proceedings of the 9th international conference on User modeling
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
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Users of web sites often do not know exactly what they are looking for or what the site has to offer. During navigation they use the information found so far to formulate their information needs and refine their search. In these cases users need to pass through a series of pages before they can use the information that will eventually answer their question. Recommender systems aimed at leading users to target pages directly do not provide optimal assistance to these users. In this paper we propose a method to automatically divide web navigation into a number of stages. A recommender can use these stages to recommend pages which do not only match the topic of a user's search, but also the current stage of the navigation process. As these recommendations are more tailored toward the user's current situation, they can provide better assistance than recommendations made by traditional recommender systems.