This study evaluates the performance of various recommendation system algorithms using anonymized real-World data sets from a commercial group in the retail sector located in the northern coast of Colombia. We evaluate several Recommender System algorithms, including Singular Value Decomposition (SVD), Non-Negative Matrix Factorization (NNMF), and collaborative filtering techniques, coupled with different data normalization techniques. The results indicate that the performance of these algorithms varies significantly depending on the data volume and normalization methods used. The study highlights the potential of Recommendation Algorithms for the Colombian Retail Sector to increase sales and improve inventory turnover. Future research should explore memory-based techniques and the integration of additional data sources, such as social media and web scraping, to further enhance recommendation accuracy and relevance.