论文标题
排名对象的预算约束
Budget-Constrained Reinforcement of Ranked Objects
论文作者
论文摘要
商业条目，例如酒店，根据搜索引擎或推荐系统的评分进行排名，并且可以通过进行有针对性的投资（例如广告）来提高每个商业的分数。我们研究拥有或支持一组条目的委托人如何最佳分配预算以最大化其排名的问题。将排名分数集代表分数的概率分布，我们将此问题视为分布之间的游戏。 我们表明，在一般情况下，最好的排名是通过均衡几个不相交得分范围的得分来实现的。我们表明，存在一种独特的最佳加固策略，并提供了实施它的有效算法。
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.
