Research Article Template with Citations and BibTeX

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Online at https://diogoribeiro7.github.io/analytics-blog-jekyll/2024/04/06/research-paper-with-citations/

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Abstract

We evaluate adaptive experimentation for recommendation systems, focusing on policy regret minimization across cold-start cohorts. Empirical results indicate a 12% lift in engagement relative to static baselines while maintaining fairness constraints.

Introduction

Personalized experiences need to balance accuracy and fairness. Prior work on contextual bandits [1] and on fairness constraints in classification [2] lays the foundation for our framework.

Methodology

We define policy regret as

\begin{equation}\label{eq:regret} \mathcal{R}_T = \sum_{t=1}^T \bigl( r_t(x_t, a_t^\star) - r_t(x_t, a_t) \bigr) \end{equation}

where $r_t$ is the reward and $a_t^\star$ is the action chosen by an oracle. Algorithm 1 summarizes the constrained Thompson sampling procedure, whose regret guarantees follow the classical analysis [3, 4].

Initialize posterior priors for all arms
for each round t = 1..T:
  sample reward estimates from posterior
  project samples to satisfy fairness constraints
  choose arm with highest adjusted draw
  update posterior with observed reward

Results

Metric Baseline Adaptive policy
Click-through rate 5.4% 6.1%
Retention (28-day) 42.0% 45.8%
Fairness gap (Δ) 0.17 0.06

Discussion

Equation \eqref{eq:regret} highlights how regret decomposes into reward differences, as in the contextual setting of [1]. Posterior sampling adapts to the cold-start cohorts without a tuned exploration rate [4, sec. 7]. Future work will incorporate causal constraints to prevent drift.

Cite this work

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@article{ribeiro2024adaptive,
  title = {Adaptive Recommendation Under Fairness Constraints},
  author = {Ribeiro, Diogo and Smith, Ada},
  journal = {Journal of Responsible AI},
  year = {2024},
  volume = {12},
  number = {2},
  pages = {45--63},
  doi = {10.1234/jrai.2024.5678}
}

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    References

    1. Li, L., Chu, W., Langford, J. and Schapire, R. E. (2010). A Contextual-Bandit Approach to Personalized News Article Recommendation. Proceedings of the 19th International Conference on World Wide Web, 661–670. ACM. https://doi.org/10.1145/1772690.1772758 ↩ a b
    2. Zafar, M. B., Valera, I., Gomez Rodriguez, M. and Gummadi, K. P. (2017). Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. Proceedings of the 26th International Conference on World Wide Web, 1171–1180. https://doi.org/10.1145/3038912.3052660 ↩
    3. Agrawal, S. and Goyal, N. (2012). Analysis of Thompson Sampling for the Multi-armed Bandit Problem. Proceedings of the 25th Annual Conference on Learning Theory, 23, 39.1–39.26. https://proceedings.mlr.press/v23/agrawal12.html ↩
    4. Russo, D. J., Van Roy, B., Kazerouni, A., Osb, Ian, ,. and Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends in Machine Learning, 11(1), 1–96. https://doi.org/10.1561/2200000070 ↩ a b

    © 2024 Diogo Ribeiro. Text and figures under CC BY 4.0.

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    Diogo Ribeiro (2024). Research Article Template with Citations and BibTeX. DataLog | Data Science & Research Theme. https://diogoribeiro7.github.io/analytics-blog-jekyll/2024/04/06/research-paper-with-citations/.

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