自适应信息访问中的个性化-隐私权衡

Barry Smyth
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引用次数: 2

摘要

随着在线信息继续以指数级速度增长,我们有效访问这些信息的能力却没有提高,用户经常因为快速轻松地找到正确的信息是多么困难而感到沮丧。所谓的个性化技术是这种信息过载问题的潜在解决方案:通过自动学习用户的需求和偏好,个性化信息访问解决方案有可能为用户提供一种更主动、更智能的信息访问形式,这种形式对用户的长期偏好和当前需求很敏感。在本文中,我们记录了两个使用个性化技术来支持信息浏览和搜索的案例研究。此外,我们考虑了不可避免的隐私问题,这些问题与分析和个性化技术齐头并进,并强调了在实际系统的开发和部署中,在隐私和个性化之间取得适当平衡的重要性。
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Personalization-Privacy Tradeoffs in Adaptive Information Access
As online information continues to grow at an exponential rate our ability to access this information effectively does not, and users are often frustrated by how difficult it is to locate the right information quickly and easily. So-called personalization technology is a potential solution to this information overload problem: by automatically learning about the needs and preferences of users, personalized information access solutions have the potential to offer users a more proactive and intelligent form of information access that is sensitive to their long-term preferences and current needs. In this paper, we document two case-studies of the use of personalization techniques to support information browsing and search. In addition, we consider the inevitable privacy issues that go hand-in-hand with profiling and personalization techniques and highlight the importance of striking the right balance between privacy and personalization when it comes to the development and deployment of practical systems.
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Personalization Techniques and Recommender Systems An Experimental Study of Feature Selection Methods for Text Classification Identifying and Analyzing User Model Information from Collaborative Filtering Datasets Personalization-Privacy Tradeoffs in Adaptive Information Access User Acceptance of Knowledge-based Recommenders
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