Contemporary recommender systems act as intermediaries on multi-sided platforms serving high utility recommendations from sellers to buyers. Such systems attempt to balance the objectives of multiple stakeholders including sellers, buyers, and the platform itself. The difficulty in providing recommendations that maximize the utility for a buyer, while simultaneously representing all the sellers on the platform has lead to many interesting research problems.Traditionally, they have been formulated as integer linear programs which compute recommendations for all the buyers together in an \emph{offline} fashion, by incorporating coverage constraints so that the individual sellers are proportionally represented across all the recommended items. Such approaches can lead to unforeseen biases wherein certain buyers consistently receive low utility recommendations in order to meet the global seller coverage constraints. To remedy this situation, we propose a general formulation that incorporates seller coverage objectives alongside individual buyer objectives in a real-time personalized recommender system. In addition, we leverage highly scalable submodular optimization algorithms to provide recommendations to each buyer with provable theoretical quality bounds. Furthermore, we empirically evaluate the efficacy of our approach using data from an online real-estate marketplace.

翻译：现代推荐人系统作为多面平台的中间人,为卖方向买方提供高效用建议。这种系统试图平衡包括卖方、买方和平台本身在内的多个利益攸关方的目标。提供建议,使买方的效用最大化,同时在平台上代表所有卖方,这种困难导致许多有趣的研究问题。 传统上,这些系统是作为整数线性程序拟订的,它以一种可变的理论质量约束的方式,为所有买方一起计算建议,纳入覆盖面限制,使个别卖方在所有建议项目中的比例代表。这种方法可能导致意外的偏差,即某些买方一贯收到低效用建议,以满足全球卖方的覆盖范围限制。为了纠正这种情况,我们提出了一个一般性的提法,在实时个人化建议系统中将卖方的覆盖范围目标与个别买方目标结合起来。此外,我们利用高度可扩展的亚式微调优化算法,向每个买方提供建议,同时提供可验证的理论质量约束。此外,我们用经验评估我们使用在线房地产市场数据的方法的功效。