How many bits per rating?

@inproceedings{Kluver2012HowMB,
  title={How many bits per rating?},
  author={Daniel Kluver and Tien T. Nguyen and Michael D. Ekstrand and Shilad Sen and John Riedl},
  booktitle={RecSys},
  year={2012}
}
Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in ratings and predictions. We computationally explore the properties of our model and apply our framework to estimate the efficiency of different rating… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 19 CITATIONS

References

Publications referenced by this paper.