Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ANATAGONOMY: a personalized newspaper on the World Wide Web
International Journal of Human-Computer Studies - Special issue: innovative applications of the World Wide Web
Learning personal preferences on online newspaper articles from user behaviors
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Multimedia Systems
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A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
The role of user profiles for news filtering
Journal of the American Society for Information Science and Technology
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Communications of the ACM - The Adaptive Web
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
The Role of Structured Content in a Personalized News Service
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 7 - Volume 7
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International Journal of Human-Computer Studies
Web Intelligence and Agent Systems
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Proceedings of the 16th international conference on World Wide Web
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Motivations in personalisation behaviour
Interacting with Computers
Editorial: Measuring the impact of personalization and recommendation on user behaviour
International Journal of Human-Computer Studies
Bias in algorithmic filtering and personalization
Ethics and Information Technology
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Personalizing news content requires to choose the appropriate depth of personalization and to assess the extent to which readers' explicit expressions of interest in general and specific news topics can be used as the basis for personalization. A preliminary survey examined 117 respondents' attitudes towards news content personalization and their interest in various news topics and subtopics. The second survey examined 23 participants' declared and actual interests. Participants preferred personalization based on general news topics. Declared interest in general news topics adequately predicted the actual interests in some topics, while in others users' interests differed between general news topics and subtopics. The variance in interest in items also differed among topics. Thus, different personalization methods should be used for different topics. For some, such as 'Sports', users show either high interest or no interest at all. In the latter case most articles related to the topic should be removed, with the exception of items that refer to unique events that may raise general interest according to the expressed interest. In other topics, such as 'Science & Technology', most users are interested in important articles, even if they are not interested in the general news topic. Here, the filtering technique should identify the important articles and present them to all readers. The results can be used to develop effective and simple personalization mechanisms which can be applied to the personalization of news, as well as to other domains.