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By the growing trend of online shopping and e-commerce websites,recommendation systems have gained more importance in recent years in order toincrease the sales ratios of companies. Different algorithms on recommendationsystems are used and every one produce different results. Every algorithm onthis area have positive and negative attributes. The purpose of the research isto test the different algorithms for choosing the best one according asstructure of dataset and aims of developers. For this purpose, threshold andk-means based collaborative filtering and content-based filtering algorithmsare utilized on the dataset contains 100*73421 matrix length. What are thedifferences and effects of these different algorithms on the same dataset? Whatare the challenges of the algorithms? What criteria are more important in orderto evaluate a recommendation systems? In the study, we answer these crucialproblems with the case study.

Comparison of the Efficiency of Different Algorithms on Recommendation System Design: a Case Study