Performance comparison and analysis of SVD and ALS in recommendation system

This research predominantly focuses on the electronic segment of the Amazon dataset. In this setting, this study’s primary objective is to use this particular dataset to carry out a detailed comparative analysis of two matrix factorization-based collaborative filtering techniques, namely Singular Value Decomposition (SVD) and Alternating Least Squares (ALS). The findings stemming from this investigation reveal a notable contrast in the performance of these algorithms. Specifically, the SVD algorithm demonstrates significantly higher overall accuracy when compared to ALS. This observation suggests that in scenarios characterized by denser and smaller datasets, the SVD algorithm outperforms ALS by a considerable margin. The implications of these results underscore the significance of algorithm selection in recommender systems, emphasizing that the performance of collaborative filtering methods can vary markedly depending on the dataset’s characteristics. Additionally, this research highlights the potential limitations of ALS in scenarios similar to the one explored here, shedding light on the importance of tailoring algorithmic choices to the specific data environment. Overall, these findings contribute valuable insights to the field of recommendation systems and provide guidance for algorithm selection based on dataset properties.