自适应信息访问和对个性化隐私最佳点的追求

Barry Smyth
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引用次数: 2

摘要

2000年,整个万维网仅包含21tb的信息;现在它以每天3倍的速度增长。这种惊人的增长构成了信息超载的问题,这一问题正威胁着信息革命的发展。简而言之,用户越来越难以在正确的时间以正确的方式找到正确的信息。搜索引擎技术正在努力处理大量可用的信息,而网络用户显然无法制定有效的搜索查询,以准确反映他们当前的信息需求,这极大地加剧了这个问题。这次演讲将集中讨论如何使用所谓的个性化技术来应对信息过载问题。个性化研究汇集了人工智能、用户分析、信息检索和用户界面设计等领域的思想,为用户提供更主动、更智能的信息服务,能够预测个人的需求,并适应他们的隐性偏好。我们将描述个性化技术如何成功地应用于两种主要的信息访问模式,即浏览和搜索,并参考在移动互联网和Web搜索领域部署的应用程序。我们将特别关注个性化的潜在价值与与分析相关的感知隐私风险之间存在的自然紧张关系。我们将重点介绍一些最近的个性化方法,这些方法似乎在个性化和隐私之间取得了有益的平衡,并认为实现这种个性化-隐私的最佳点可能是未来个性化技术大规模成功的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive information access and the quest for the personalization-privacy sweetspot
In 2000 the entire World-Wide Web consisted of just 21 terabytes of information; now it grows by 3 times this every single day. This phenomenal growth frames the information overload problem that is threatening to stall the information revolution going forward. In short, users are finding it increasingly difficult to locate the right information at the right time in the right way. Search engine technologies are struggling to cope with the sheer quantity of information that is available, a problem that is greatly exacerbated by the apparent inability of Web users to formulate effective search queries that accurately reflect their current information needs. This talk will focus on how so-called personalization techniques are being used in response to the information overload problem.Personalization research brings together ideas from artificial intelligence, user profiling, information retrieval and user-interface design to provide users with more proactive and intelligent information services that are capable of predicting the needs of individuals and adapting to their implicit preferences. We will describe how personalization techniques have been successfully applied to the two dominant modes of information access, browsing and search, with reference to deployed applications in the mobile Internet and Web search arenas. Particular attention will be paid to the natural tension that exists between the potential value of personalization, on the one hand, and the perceived privacy risk associated with profiling, on the other. We will highlight certain recent approaches to personalization that appear to achieve a useful balance between personalization and privacy and argue that realizing this personalization-privacy sweet spot may be the key to the large-scale success of personalization technologies in the future.
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