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A Trustable Recommender System for Web Content

Russ Greiner, Dept of Computing Science; PI of AICML
Tingshao Zhu
Gerald Haeubl, School of Business; University of Alberta
Bob Price
Kevin Jewell, Alberta Ingenuity

This paper presents three principles for building a Web recommender system that can be trusted to provide valuable page recommendations to Web users. We also demonstrate the implementation of these principles in an effective complete-Web recommender system --- WebIC. WebIC is able to predict relevant pages based on a learned model, whose parameters are estimated from a labelled corpus. Data from a recent user study demonstrate that the prediction model can recommend previously unseen pages of high relevance from anywhere on the Web for any user.

Citation

R. Greiner, T. Zhu, G. Haeubl, B. Price, K. Jewell. "A Trustable Recommender System for Web Content". Beyond Personalization, January 2005.

Keywords:	WebIC, machine learning
Category:	In Workshop

BibTeX

@misc{Greiner+al:IUI-BeyondPersonalization05,
  author = {Russ Greiner and Tingshao Zhu and Gerald Haeubl and Bob Price and
    Kevin Jewell},
  title = {A Trustable Recommender System for Web Content},
  booktitle = {Beyond Personalization},
  year = 2005,
}

Last Updated: April 23, 2007
Submitted by Nelson Loyola

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A Trustable Recommender System for Web Content

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