We consider the problem of slate recommendation, where the recommender system presents a user with a collection or slate composed of K recommended items at once. If the user finds the recommended items appealing then the user may click and the recommender system receives some feedback. Two pieces of information are available to the recommender system: was the slate clicked? (the reward), and if the slate was clicked, which item was clicked? (rank). In this paper, we formulate several Bayesian models that incorporate the reward signal (Reward model), the rank signal (Rank model), or both (Full model), for non-personalized slate recommendation. In our experiments, we analyze performance gains of the Full model and show that it achieves significantly lower error as the number of products in the catalog grows or as the slate size increases.

翻译：推荐人系统同时向用户提供由 K 推荐项目组成的集合或列表。 如果用户发现推荐项目具有吸引力, 用户可以点击, 推荐人系统可以收到一些反馈。 推荐人系统可得到两部分信息: 选择点击了吗? (奖赏), 如果选择人被点击, 哪个项目被点击? (级别 ) 。 在本文中, 我们为非个性化的列表建议, 设计了几种贝叶斯模式, 包括奖赏信号( 奖赏模式)、 排名信号( Rank 模式) 或两者( Full 模式 ) 。 在实验中, 我们分析全模型的绩效收益, 并显示随着目录中产品数量的增长或缩放大小的增加, 它的错误率要低得多 。