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@@ -158,7 +158,7 @@ The nightly build tests are run daily on AzureML.
 
 ## References
 
-- D. Li, J. Lian, L. Zhang, K. Ren, D. Lu, T. Wu, X. Xie, "Recommender Systems: Frontiers and Practices" (in Chinese), Publishing House of Electronics Industry, Beijing 2022.
+- D. Li, J. Lian, L. Zhang, K. Ren, D. Lu, T. Wu, X. Xie, "Recommender Systems: Frontiers and Practices", Springer, Beijing, 2024. [Available on this link](https://www.amazon.com/Recommender-Systems-Frontiers-Practices-Dongsheng/dp/9819989639/).
 - A. Argyriou, M. González-Fierro, and L. Zhang, "Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems", *WWW 2020: International World Wide Web Conference Taipei*, 2020. Available online: https://dl.acm.org/doi/abs/10.1145/3366424.3382692
-- L. Zhang, T. Wu, X. Xie, A. Argyriou, M. González-Fierro and J. Lian, "Building Production-Ready Recommendation System at Scale", *ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2019 (KDD 2019)*, 2019.
 - S. Graham,  J.K. Min, T. Wu, "Microsoft recommenders: tools to accelerate developing recommender systems", *RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems*, 2019. Available online: https://dl.acm.org/doi/10.1145/3298689.3346967
+- L. Zhang, T. Wu, X. Xie, A. Argyriou, M. González-Fierro and J. Lian, "Building Production-Ready Recommendation System at Scale", *ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2019 (KDD 2019)*, 2019.