  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.
