An adaptive Web page recommendation service
An adaptive Web page recommendation service
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自适应网页推荐服务
DOI：
10.1145/267658.267744
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
M. Balabanovic
中科院分区：
文献类型：
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作者：
M. Balabanovic
自适应推荐服务寻求适应其用户，随着时间的推移提供越来越个性化的推荐。在本文中，我们介绍了“Fab”自适应网页推荐服务。已经有很多关于分析文档内容以改进推荐或搜索结果的研究。最近，研究人员开始探索如何利用用户之间的相似性来达到相同的目的。Fab系统在这两种方法之间取得了平衡，利用了用户之间的共同兴趣，而不会失去内容分析提供的表示的好处。自1996年3月运行以来，它已经填充了一个收集和选择网页，其互动促进紧急协作属性的代理集合。在本文中，我们解释了系统架构的设计，并报告了我们的第一个实验的结果，评估建议提供给一组测试用户。
An adaptive recommendation service seeks to adapt to its users, providing increasingly personalized recommendations over time. In this paper we introduce the "Fab" adaptive web page recommendation service. There has been much research on analyzing document content in order to improve recommendations or search results. More recently researchers have begun to explore how the similarities between users can be exploited to the same ends. The Fab system strikes a balance between these two approaches, taking advantage of the shared interests among users without losing the benefits of the representations provided by content analysis. Running since March 1996, it has been populated with a collection of agents for the collection and selection of web pages, whose interaction fosters emergent collaborative properties. In this paper we explain the design of the system architecture and report the results of our first experiment, evaluating recommendations provided to a group of test users.