Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Capturing knowledge of user preferences: ontologies in recommender systems
Proceedings of the 1st international conference on Knowledge capture
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Collaborative feed reading in a community
Proceedings of the ACM 2009 international conference on Supporting group work
Improving web search relevance and freshness with content previews
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Development and performance evaluation of a new RSS tool for a Web-based system: RSS_PROYECT
Journal of Network and Computer Applications
Personalized web feeds based on ontology technologies
Information Systems Frontiers
An efficient and scalable ranking technique for mashups involving RSS data sources
Journal of Network and Computer Applications
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In this paper a novel article ranking method called NectaRSS is introduced. The system recommends incoming articles, which we will designate as newsitems, to users based on their past choices. User preferences are automatically acquired, avoiding explicit feedback, and ranking is based on those preferences distilled to a user profile. NectaRSS uses the well-known vector space model for user profiles and new documents, and compares them using information retrieval techniques, but introduces a novel method for user profile creation and adaptation from users' past choices. The efficiency of the proposed method has been tested by embedding it into an intelligent aggregator (RSS feed reader) which has been used by different and heterogeneous users. Besides, this paper proves that the ranking of newsitems yielded by NectaRSS improves its quality with user's choices, and its superiority over other algorithms that use a different information representation method.