DeepAI AI Chat
Log In Sign Up

Budget-Constrained Reinforcement of Ranked Objects

by   Amir Ban, et al.

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.


page 7

page 8


Ensemble- and Distance-Based Feature Ranking for Unsupervised Learning

In this work, we propose two novel (groups of) methods for unsupervised ...

Predicting Preference Flips in Commerce Search

Traditional approaches to ranking in web search follow the paradigm of r...

Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017

This paper describes our participation in the Triple Scoring task of WSD...

Comment Ranking Diversification in Forum Discussions

Viewing consumption of discussion forums with hundreds or more comments ...

Ties in ranking scores can be treated as weighted samples

Prior proposals for cumulative statistics suggest making tiny random per...

Judging the Judges: Evaluating the Performance of International Gymnastics Judges

Judging a gymnastics routine is a noisy process, and the performance of ...