Lexical analysis and stoplists
Information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Little words can make a big difference for text classification
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Optimization of relevance feedback weights
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning in the presence of concept drift and hidden contexts
Machine Learning
Incremental relevance feedback for information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
An adaptive Web page recommendation service
AGENTS '97 Proceedings of the first international conference on Autonomous agents
A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
An adaptive algorithm for learning changes in user interests
Proceedings of the eighth international conference on Information and knowledge management
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Machine Learning
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning to surf: multiagent systems for adaptive web page recommendation
Learning to surf: multiagent systems for adaptive web page recommendation
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In recent years, an increasing interest in recommendation systems has emerged both from the research and the application point of view and in both academic and commercial domains. The majority of comparison techniques used for formulating recommendations are based on set-operations over user-supplied terms or internal product computations on vectors encoding user preferences. In both cases however, the "identical-ness" of terms is examined rather than their actual semantic relevance. This paper proposes a recommendation algorithm that is based on the maintenance of user profiles and their dynamic adjustment according to the users" behavior. Moreover, this algorithm relies on the dynamic management of communities, which contain "similar" and "relevant" users and which are created according to a classification algorithm. The algorithm is implemented on top of a community management mechanism. The comparison mechanism used in the context of this work is based on semantic relevance between terms, which is evaluated with the use of a glossary of terms.