论文标题
福利优化的推荐系统
Welfare-Optimized Recommender Systems
论文作者
论文摘要
我们提出了一个基于随机实用程序模型的推荐系统。在线购物者被建模为具有有限信息的理性决策者，并且建议任务被提出为最佳地富含购物者意识套装的问题。值得注意的是，价格信息和购物者的愿望扮演着关键的角色。此外，为了更好地说明推荐的商业性质，我们将零售商和购物者的矛盾目标统一为单一的福利指标，我们建议这是一个新的建议目标。我们在合成数据上测试我们的框架，并在各种场景中显示其性能。推荐系统文献中没有这个新框架，为福利优化的推荐系统，优惠券和价格优化打开了大门。
We present a recommender system based on the Random Utility Model. Online shoppers are modeled as rational decision makers with limited information, and the recommendation task is formulated as the problem of optimally enriching the shopper's awareness set. Notably, the price information and the shopper's Willingness-To-Pay play crucial roles. Furthermore, to better account for the commercial nature of the recommendation, we unify the retailer and shoppers' contradictory objectives into a single welfare metric, which we propose as a new recommendation goal. We test our framework on synthetic data and show its performance in a wide range of scenarios. This new framework, that was absent from the Recommender System literature, opens the door to Welfare-Optimized Recommender Systems, couponing, and price optimization.
