In recent years, popularity bias has consistently accompanied the development of recommender systems, significantly impacting their fairness. Currently, numerous studies have been conducted to explore this topic, such as Causal Inference and Contrastive Learning. However, while these methods have alleviated popularity bias to some extent, most of them have not fully exploited the rich information embedded in item popularity within user sequences. To better utilize this type of information, we propose a novel method-the Item Popularity Attention Recommender System (IPARec). By introducing item popularity, IPARec can further mitigate popularity bias. This method incorporates item popularity embeddings and an attention mechanism, allowing the model to effectively capture user interests while considering the impact of item popularity on recommendations, thus addressing the popularity bias issue more comprehensively. The proposed IPARec effectively models the interplay of item popularity within user sequences and achieves outstanding performance across various datasets. IPARec significantly improves the recommendation of unpopular items, showcasing its unique advantage in alleviating popularity bias. With these advantages, IPARec not only improves the diversity and fairness of recommendations but also offers an innovative solution to address popularity bias in recommender systems.