<p>Understanding products and customers is a critical challenge for efficient business operations. While various machine learning-based analytical methods have been proposed, most rely on objective metrics such as evaluation scores or tags. However, estimating subjective evaluation scores is also an essential aspect of understanding customers, yet research in this area remains limited. Moreover, it is well-known that directly evaluating the subjective scores of targets is challenging. Consequently, traditional methods have used pairwise comparisons between targets to estimate true evaluation scores. However, as the number of targets increases, the required number of pairwise comparisons grows exponentially, making it difficult to estimate subjective evaluations for a large number of targets using conventional methods. To address this issue, this study proposes a scalable model for subjective evaluation score estimation by completing pairwise comparison data using a deep learning model trained on a limited number of annotations. Specifically, the deep learning model is trained on pairwise comparison results from a subset of evaluation target combinations annotated by humans, and the model’s predictions are used to complete the pairwise comparison matrix. The effectiveness and practical applicability of the proposed method are demonstrated through applications to multiple real-world datasets.</p>