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User attitudes towards news content personalization

机译：用户对新闻内容个性化的态度

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摘要

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.
机译：个性化新闻内容需要选择适当的个性化深度，并评估读者对一般和特定新闻主题的明确兴趣表达可以用作个性化基础的程度。初步调查研究了117位受访者对新闻内容个性化的态度以及他们对各种新闻主题和副主题的兴趣。第二次调查检查了23名参与者的宣告利益和实际利益。参与者倾向于根据一般新闻主题进行个性化设置。对一般新闻主题的宣称兴趣可以充分预测某些主题的实际兴趣，而在其他新闻主题中，用户兴趣在一般新闻主题和子主题之间有所不同。主题之间的兴趣差异也有所不同。因此，对于不同的主题应使用不同的个性化方法。对于某些内容（例如“体育”），用户表现出很高的兴趣或根本没有兴趣。在后一种情况下，应删除与该主题相关的大多数文章，但引用涉及唯一事件的项目除外，这些事件可能会根据表达的兴趣引起普遍关注。在其他主题（例如“科学与技术”）中，即使对通用新闻主题不感兴趣，大多数用户也对重要文章感兴趣。在这里，过滤技术应识别重要文章并将其呈现给所有读者。结果可用于开发有效且简单的个性化机制，该机制可应用于新闻以及其他领域的个性化。

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