A language model-based framework for multi-publisher content-based recommender systems |
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| Authors: | Hamed?Zamani Email author" target="_blank">Azadeh?ShakeryEmail author |
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| Institution: | 1.School of Electrical and Computer Engineering, College of Engineering,University of Tehran,Tehran,Iran;2.School of Computer Science,Institute for Research in Fundamental Sciences (IPM),Tehran,Iran |
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| Abstract: | 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. |
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