I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems

@inproceedings{Amatriain2009ILI,
  title={I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems},
  author={Xavier Amatriain and Josep M. Pujol and Nuria Oliver},
  booktitle={UMAP},
  year={2009}
}
  • Xavier Amatriain, Josep M. Pujol, Nuria Oliver
  • Published in UMAP 2009
  • Computer Science
  • Recent growing interest in predicting and influencing consumer behavior has generated a parallel increase in research efforts on Recommender Systems. Many of the state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order to later predict unknown ratings. An underlying assumption in this approach is that the user ratings can be treated as ground truth of the user's taste. However, users are inconsistent in giving their feedback, thus introducing an unknown amount of… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 16 REFERENCES

    The Netflix Prize

    VIEW 1 EXCERPT

    Reflecting and deflecting stereotypes : Assimilation and contrast in impression formation and automatic behavior

    • X. Li M. Harper, Y. Chen, J.
    • J . of Exp . Social Psych .
    • 2001

    Davidshofer.Psychological testing: Principles and applications (4th edition)

    • C. K. Murphy
    • Addison-Welsley,
    • 1996
    VIEW 1 EXCERPT