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User attitudes towards news content personalization
Authors:Talia Lavie  Michal Sela  Ilit Oppenheim  Ohad Inbar  Joachim Meyer
Affiliation:1. Computer Science Department, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Santiago, Chile;2. School of Information Sciences, University of Pittsburgh, 135 North Bellefield Ave., Pittsburgh, PA 15260, USA;1. School of Information Science and Engineering, Xiamen University, Xiamen 361005, China;2. School of Computer Science, Florida International University, Miami, FL 33199, USA;1. School of Computer Science, Florida International University, Miami, FL 33199, USA;2. School of Information Science and Technology, Xiamen University, Xiamen 361005, China;1. Product Design and Manufacture Group, Faculty of Engineering, University of Nottingham China, Ningbo 315100, China;2. Loughborough Design School, Loughborough University, Ashby Road, Loughborough LE11 3TU, UK;3. Department of Quantitative and Applied Economics, Nottingham University Business School, University of Nottingham China, Ningbo 315100, China
Abstract: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.
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