Ranking models have achieved promising results, but it remains challenging to design personalized ranking systems to leverage user profiles and semantic representations between queries and documents. In this paper, we propose a topic-based personalized ranking model (TPRM) that integrates user topical profile with pretrained contextualized term representations to tailor the general document ranking list. Experiments on the real-world dataset demonstrate that TPRM outperforms state-of-the-art ad-hoc ranking models and personalized ranking models significantly.

翻译：排名模式取得了有希望的成果,但设计个性化排名系统以利用用户概况和查询与文件之间的语义表达仍然具有挑战性。 在本文件中,我们提议了基于主题的个性化排名模式(TPRM ), 将用户专题简介与事先经过培训的背景化术语表述结合起来,以调整一般文件排名清单。 现实世界数据集实验显示TPRM明显优于最先进的特设排名模式和个性化排名模式。