The rapid growth of the Web has increased the difficulty of finding the information that can address the users’ information needs. A number of recommendation approaches have been developed to tackle this problem. The increase in the number of data providers has necessitated the development of multi-publisher recommender systems; systems that include more than one item/data provider. In such environments, preserving the privacy of both publishers and subscribers is a key and challenging point. In this paper, we propose a multi-publisher framework for recommender systems based on a client–server architecture, which preserves the privacy of both data providers and subscribers. We develop our framework as a content-based filtering system using the statistical language modeling framework. We also introduce AUTO, a simple yet effective threshold optimization algorithm, to find a dissemination threshold for making acceptance and rejection decisions for new published documents. We further propose a language model sketching technique to reduce the network traffic between servers and clients in the proposed framework. Extensive experiments using the TREC-9 Filtering Track and the CLEF 2008-09 INFILE Track collections indicate the effectiveness of the proposed models in both single- and multi-publisher settings.

中文翻译：

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用于基于多发布者内容的推荐系统的基于语言模型的框架

Web的快速发展增加了查找可满足用户信息需求的信息的难度。已经开发了许多推荐方法来解决这个问题。数据提供者数量的增加已导致必须开发多发布者推荐系统; 包含多个项目/数据提供者的系统。在这样的环境中，保护发布者和订阅者的隐私是关键和具有挑战性的一点。在本文中，我们提出了一种基于客户端-服务器体系结构的推荐系统的多发布者框架，该框架保留了数据提供者和订阅者的隐私。我们使用统计语言建模框架将框架开发为基于内容的过滤系统。我们还引入了AUTO，这是一种简单而有效的阈值优化算法，用于查找用于为新发布的文档做出接受和拒绝决策的传播阈值。我们进一步提出了一种语言模型草图绘制技术，以减少所提出框架中服务器和客户端之间的网络流量。