CBMR: An optimized MapReduce for item-based collaborative filtering recommendation algorithm with empirical analysis

Item-based collaborative filtering (CF) is amodel-based algorithm for making recommendations. In the algorithm, the similarity between items are calculated by using a number of similarity measures, and then these similarity values are used to predict ratings for users. However, if the number of items and users grows to millions, the scalability and the processing efficiency of item-based CF can be hindered by some hardware constraints. To solve this problem, we propose an optimized MapReduce for item-based CF algorithm integrated with empirical analysis. Through extensive experiments on real-world datasets, we demonstrate the advantages of our approach by evaluating its execution time and by comparing its shuffle phase overhead with the conventional methods. The experimental results suggest that our approach has better performance when processing large-scale datasets.