Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Personalization in Context: Does Context Matter When Building Personalized Customer Models?
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Construction of Ontology-Based User Model for Web Personalization
UM '07 Proceedings of the 11th international conference on User Modeling
BIS'07 Proceedings of the 10th international conference on Business information systems
Dynamic adaptation strategies for long-term and short-term user profile to personalize search
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Representing context in web search with ontological user profiles
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Contextual search using ontology-based user profiles
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Cloning mechanisms to improve agent performances
Journal of Network and Computer Applications
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A key feature in developing an effective web personalization system is to build and model dynamic user profiles. In this paper, we propose a multi-agent approach for building a dynamic user profile that is effectively capable of learning and adapting to user behaviour. The main goal is to implicitly track user browsing behaviour in order to extract short-term and long-term user interests. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology. In this paper, we focus on the learning and the adaptation processes that are essential in modelling a dynamic user profile. Our proposed model has been integrated with a personalized search system and experiments show that our system is able to effectively model a dynamic user profile that is capable of learning and adapting to user behaviour. Experiments also show that our model achieved a higher performance than non-personalized system.