In reality， recommender systems usually suffer from various bias problems， such as exposure bias， position bias and selection bias. A recommendation model that ignores the bias problems cannot reflect the real performance of the recommender system， and may be untrustworthy for users. Previous works show that a recommendation model based on propensity score estimation can effectively alleviate the exposure bias problem of implicit feedback data in recommender systems， but only item information is usually considered to estimate propensity scores， which may lead to inaccurate estimation of propensity scores. To improve the accuracy of propensity score estimation， a Match Propensity Estimator （MPE） method was proposed. Specifically， a concept of users’ popularity preference was introduced at first， and then more accurate modeling of the sample exposure rate was achieved by calculating the matching degree of the user’s popularity preference and the item’s popularity. The proposed estimation method was integrated with a traditional recommendation model and an unbiased recommendation model， and the integrated models were compared to three baseline models including the above two models. Experimental results on a public dataset show that the models combining MPE method achieve significant improvement on three evaluation metrics such as recall， Discounted Cumulative Gain （DCG） and Mean Average Precision （MAP） compared with the corresponding baseline models respectively. In addition， experimental results demonstrate that a large part of the performance gain comes from long-tail items， showing that the proposed method is helpful to improve the diversity and coverage of recommended items.