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.
翻译:我们提出了一个基于随机实用模型的建议系统。在线购物者以信息有限的理性决策者为模范,而建议任务则被确定为最佳地丰富直升机意识的问题。值得注意的是,价格信息和直升机的意志到交易的作用非常关键。此外,为了更好地说明建议的商业性质,我们将零售商和顾客相互矛盾的目标统一成一个单一的福利指标,我们提议将其作为一个新的建议目标。我们测试了我们的合成数据框架,并展示了其在多种情景下的绩效。这个在建议系统文献中缺失的新框架打开了福利-优化建议系统、配对制和价格优化的大门。