Typical recommender systems push K items at once in the result page in the form of a feed, in which the selection and the order of the items are important for user experience. In this paper, we formalize the K-item recommendation problem as taking an unordered set of candidate items as input, and exporting an ordered list of selected items as output. The goal is to maximize the overall utility, e.g. the click through rate, of the whole list. As one solution to the K-item recommendation problem under this proposition, we proposed a new ranking framework called the Evaluator-Generator framework. In this framework, the Evaluator is trained on user logs to precisely predict the expected feedback of each item by fully considering its intra-list correlations with other co-exposed items. On the other hand, the Generator will generate different sequences from which the Evaluator will choose one sequence as the final recommendation. In our experiments, both the offline analysis and the online test show the effectiveness of our proposed framework. Furthermore, we show that the offline behavior of the Evaluator is consistent with the realistic online environment.
翻译:典型推荐人系统在结果页面中以种子的形式同时推K项,使项目的选择和顺序对用户经验很重要。 在本文中,我们将K项建议问题正式确定为将一组未经排序的候选项目作为输入,并将选定项目列表导出为输出。目标是最大限度地扩大整个列表的总体效用,例如按速率点击整个列表。作为本提案K项建议问题的一种解决办法,我们提议了一个称为评价员-发电机框架的新的排名框架。在这个框架内,评价员接受用户日志培训,以便通过充分考虑其与其他共同曝光项目的内在关联来准确预测每个项目的预期反馈。另一方面,发电机将产生不同的序列,使评价员从中选择一个序列作为最后建议。在我们的实验中,离线分析和在线测试都显示了我们拟议框架的有效性。此外,我们显示评价员的离线行为符合现实的在线环境。