Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH scholarly article en Hwang, Seung-won; Roh, Jong-won License
when quoting this document, please refer to the following
URN: urn:nbn:de:0030-drops-19336


Recommendation: A Less Explored Killer-App of Uncertainty?



Due to the unprecedented amount of information available, it is becoming more and more important to provide personalized recommendations on data, based on past user feedbacks. However, available user feedbacks or ratings are extremely sparse, which motivates the needs for rating prediction. The most widely adopted solution has been collaborative filtering, which (1) identifies "neighboring" users with similar tastes and (2) aggregates their ratings to predict the ratings of the given user. However, while each of such aggregation involves varying levels of uncertainty, e.g., depending on the distribution of ratings aggregated, which has not been systematically considered in recommendation, though recent study suggests such consideration can boost prediction accuracy. To consider uncertainty in rating prediction, this paper reformulates the collaborative filtering problem as aggregating community ratings into multiple predicted ratings with varying levels of certainty, based on which we identify top-k results with both high confidence and rating. We empirically study the efficiency and accuracy of our proposed framework, over a classical collaborative filtering system.

BibTeX - Entry

  author =	{Seung-won Hwang and Jong-won Roh},
  title =	{Recommendation: A Less Explored Killer-App of Uncertainty?},
  booktitle =	{Uncertainty Management in Information Systems },
  year =	{2009},
  editor =	{Christoph Koch and Birgitta K{\"o}nig-Ries and Volker Markl and Maurice van Keulen},
  number =	{08421},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{},
  annote =	{Keywords: Recommendation, uncertainty}

Keywords: Recommendation, uncertainty
Seminar: 08421 - Uncertainty Management in Information Systems
Issue date: 2009
Date of publication: 2009

DROPS-Home | Fulltext Search | Imprint Published by LZI