Evaluating Stochastic Rankings with Expected Exposure

@article{Diaz2020EvaluatingSR,
  title={Evaluating Stochastic Rankings with Expected Exposure},
  author={Fernando Diaz and Bhaskar Mitra and Michael D. Ekstrand and Asia J. Biega and Ben Carterette},
  journal={Proceedings of the 29th ACM International Conference on Information & Knowledge Management},
  year={2020}
}
  • Fernando Diaz, Bhaskar Mitra, +2 authors Ben Carterette
  • Published 2020
  • Computer Science
  • Proceedings of the 29th ACM International Conference on Information & Knowledge Management
  • We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item should receive more or less expected exposure than any other item of the same relevance grade. We argue that this principle is desirable for many retrieval objectives and scenarios, including topical diversity and fair ranking… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-3 OF 3 REFERENCES
    BPR: Bayesian Personalized Ranking from Implicit Feedback
    • 2,515
    • Highly Influential
    • PDF
    Policy Learning for Fairness in Ranking
    • 23
    • Highly Influential
    • PDF
    Fast als-based matrix factorization for explicit and implicit feedback datasets
    • 145
    • Highly Influential