This paper proposes a cost-effective and scalable memory solution for high-performance and power-efficient recommendation systems, called computational memory solution (CMS). To address the memory bandwidth challenges of the deep learning-based recommendation system, CMS offloads memory-intensive embedding operations to near-data processors equipped with large capacity and high bandwidth memory. In contrast to the other state-of-the-art near-memory processing accelerators that only support inference and have scalability restrictions, CMS supports training and scales as much as the high-speed serial interface allows. Our evaluation results show that CMS achieves up to 7.5× higher throughput and 12.2× higher power efficiency for training than state-of-the-art accelerators.