Commercial entries, such as hotels, are ranked according to score by a search engine or recommendation system, and the score of each can be improved upon by making a targeted investment, e.g., advertising. We study the problem of how a principal, who owns or supports a set of entries, can optimally allocate a budget to maximize their ranking. Representing the set of ranked scores as a probability distribution over scores, we treat this question as a game between distributions. We show that, in the general case, the best ranking is achieved by equalizing the scores of several disjoint score ranges. We show that there is a unique optimal reinforcement strategy, and provide an efficient algorithm implementing it.
@article{arxiv.2203.14305,
title = {Budget-Constrained Reinforcement of Ranked Objects},
author = {Amir Ban and Moshe Tennenholtz},
journal= {arXiv preprint arXiv:2203.14305},
year = {2022}
}