A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
PVA: a self-adaptive personal view agent system
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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CHI '02 Extended Abstracts on Human Factors in Computing Systems
User Modeling for Adaptive News Access
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PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
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Proceedings of the 16th international conference on World Wide Web
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Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
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Proceedings of the ACM 2009 international conference on Supporting group work
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Finding the local angle in national news
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Proceedings of the 22nd international conference on World Wide Web companion
SADINA: Adaptive System for Academic News Dissemination
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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While social media have achieved significant and widespread adoption as platforms for sharing information, their use as a source of data for predicting user interests has not yet been fully explored. In this paper, we present a content-based approach to modeling user interests based on Twitter. Our recommendation system uses information retrieval techniques to represent tweets and users as collections of news topics, including high-level categories (e.g., sports, politics, business) and detailed subtopics (e.g., Chicago Bulls, Mitt Romney, entrepreneurship). We discuss the design of a system that uses this information to deliver news recommendations in the form of a personalized newspaper. Finally, we describe a novel method for evaluating recommendation systems based on Twitter that involves mining Twitter data to identify explicit indicators of news interests and comparing these to retroactive system recommendations.