超越贪婪排名：基于List-CVAE的Slate优化
Beyond Greedy Ranking: Slate Optimization via List-CVAE
摘要：
推荐问题的传统解决方案通过预测分数对单个文档候选者进行分级。然而，这种方法无法从整体上优化slate，因此，通常难以捕获由页面布局和文档相互依赖引起的偏差。石板推荐问题的目的是直接找到最佳排序的文档子集（即石板），最好地服务于用户的兴趣。解决这个问题是困难的，因为所有候选文档的组合及其在页面上的显示位置都是组合爆炸。因此，我们提出了一个范式转变，从传统的解决排名问题的直接石板生成框架。在本文中，我们介绍了列表条件变分自动编码器（List-CVAE），它学习的联合分布的文件在石板上的用户响应的条件，并直接生成完整的石板。模拟和真实世界的数据上的实验表明，List-CVAE优于流行的可比排名方法一致的各种规模的文档语料库。
英文摘要：
The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores. However, this method fails to optimize the slate as a whole, and hence, often struggles to capture biases caused by the page layout and document interdepedencies. The slate recommendation problem aims to directly find the optimally ordered subset of documents (i.e. slates) that best serve users' interests. Solving this problem is hard due to the combinatorial explosion in all combinations of document candidates and their display positions on the page. Therefore we propose a paradigm shift from the traditional viewpoint of solving a ranking problem to a direct slate generation framework. In this paper, we introduce List Conditional Variational Auto-Encoders (List-CVAE), which learns the joint distribution of documents on the slate conditioned on user responses, and directly generates full slates. Experiments on simulated and real-world data show that List-CVAE outperforms popular comparable ranking methods consistently on various scales of documents corpora.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
