A language model-based framework for multi-publisher content-based recommender systems

Article

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.

Keywords

Content-based recommender system Adaptive filtering Multi-publisher recommendation Privacy preservation Language models Threshold optimization 

Notes

Acknowledgements

We would like to gratefully thank the anonymous reviewers for their invaluable suggestions. We would also like to thank Milad Nasr, Shamim Taheri, and Mostafa Dehghani for their constructive comments towards improving the paper, and Avi Arampatzis for the helpful discussions on the KUN method. This research was supported in part by a grant from the Institute for Research in Fundamental Sciences (No. CS1396-4-51). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.

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Authors and Affiliations

  1. 1.School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
  2. 2.School of Computer ScienceInstitute for Research in Fundamental Sciences (IPM)TehranIran

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