Recommender systems are known to suffer from the popularity bias problem: popular items are recommended frequently, and nonpopular ones rarely, if at all. Prior studies focused on tackling this issue by increasing the number of recommended nonpopular (long-tail) items. However, these methods ignore the users’ personal popularity preferences and increase the exposure rate of the nonpopular items indiscriminately, which may hurt the user experience because different users have diverse interests in popularity. In this work, we propose a novel debias framework with knowledge graph (AWING), which adaptively alleviates popularity bias from the users’ perspective. Concretely, we explore fine-grained preferences (including popularity preference) behind a user-item interaction by using the heterogeneous graph transformer over the knowledge graph embedded with popularity nodes and endow the preferences with explicit semantics. Based on this idea, we can manipulate how much popularity preference affects recommendation results and improves the exposure rate of nonpopular items while considering the popularity preferences of different users. Experiments on public datasets show that the proposed method AWING can effectively alleviate popularity bias and ensure the user experience at the same time. The case study further demonstrates the feasibility of AWING on the explainable recommendation task.