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首页> 外文期刊>Journal of Intelligent Information Systems >Recommender Systems Research: A Connection-Centric Survey
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Recommender Systems Research: A Connection-Centric Survey

机译：推荐系统研究：以连接为中心的调查

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Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.
机译：推荐系统试图通过基于用户偏好从通用集中选择商品的子集来减少信息过载并留住客户。虽然对推荐系统的研究源于信息检索和过滤，但该主题已稳步推进到自己的合法且富挑战性的研究领域。传统上，从基于内容的过滤与协作设计的角度研究推荐系统。但是，建议不是在真空中提供的，而是在用户和社会环境的非正式社区中提出的。因此，最终所有推荐系统都会在人与人之间建立联系，因此应该从这种角度进行调查。推荐系统文献中未充分强调此观点。因此，我们对推荐系统研究采取了面向连接的观点。我们认为，推荐具有固有的社会元素，并且最终旨在通过明确的用户建模直接或通过发现现有数据中隐含的关系间接地与人们建立联系。因此，推荐系统的特征在于它们如何建模用户以将人们聚集在一起：显式或隐式。最后，用户建模和以连接为中心的观点引发了广泛的社会问题，例如评估，目标定位以及隐私和信任，我们也简要地解决了这些问题。

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