In recent years, pairwise methods, such as Bayesian Personalized Ranking (BPR), have gained significant attention in the field of collaborative filtering for recommendation systems. Group BPR is an extension of BPR that incorporates user groups to relax the strict assumption of independence between two users. However, the reliability of its user groups may be compromised as they only focus on a few behavioral similarities. To address this problem, this paper proposes a new entropy-weighted similarity measure for implicit feedback to quantify the relation between two users and sample like-minded user groups. We first introduce the group preference into several pairwise ranking algorithms and then utilize the entropy-weighted similarity to sample groups to further improve these algorithms. Unlike other approaches that rely solely on common item ratings, our method incorporates global information into the similarity measure, resulting in a more reliable approach to group sampling. We conducted experiments on two real-world datasets and evaluated our method using different metrics. The results show that our method can construct better user groups from sparse data and produce more accurate recommendations. Our approach can be applied to a wide range of recommendation systems, and this can significantly improve the performance of pairwise ranking algorithms, making it an effective tool for pairwise ranking.