Ranking is a core task in E-commerce recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which produces a ranking score for each individual item. However, it may be sub-optimal because the scoring function applies to each item individually and does not explicitly consider the mutual influence between items, as well as the differences of users' preferences or intents. Therefore, we propose a personalized context-aware re-ranking model for E-commerce recommender systems. The proposed re-ranking model can be easily deployed as a follow-up modular after ranking by directly using the existing feature vectors of ranking. It directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list. Specifically, the Transformer applies a self-attention mechanism that directly models the global relationships between any pair of items in the whole list. Besides, we introduce the personalized embedding to model the differences between feature distributions for different users. Experimental results on both offline benchmarks and real-world online E-commerce systems demonstrate the significant improvements of the proposed re-ranking model.
翻译:排名是电子商务建议系统的一项核心任务,目的是向用户提供一份有顺序的项目清单。通常,从标签的数据集中学习排序功能,以优化全球业绩,从而产生对每个项目的排名分数。不过,它可能是次最佳的,因为评分功能个别适用于每个项目,没有明确考虑项目之间的相互影响以及用户偏好或意向的不同。因此,我们提议了电子商务建议系统个性化的背景认知重新排序模式。拟议的重新排序模式在排序后很容易作为后续模块部署,直接使用现有排名的特征矢量。它直接优化整个建议列表,采用变压器结构有效地对列表中所有项目的信息进行编码。具体地说,变压器采用一种自留机制,直接模拟整个列表中任何一对项目之间的全球关系。此外,我们引入个性化嵌入模型,以模拟不同用户的特征分布差异。离线基准的实验结果和真实世界在线电子商务系统展示了拟议的重大改进。