A/B Testing-Based Decision-Making for Recommendation Systems

Recommendation systems provide recommendations based on user behavior and content data. User behavior and content data are fed to machine learning algorithms to train them and give recommendations to the users. These algorithms need a large amount of data for a reasonable conversion rate. However, for small applications, the available amount of data is minimal, leading to high recommendation aberrations. Also, when an existing large-scale application with a high amount of available data uses a new recommendation system, it requires some time and testing to decide which recommendation algorithm is best suited to get higher conversion rates. This learning curve costs highly when the user base and data size are significantly high. In this paper, A/B testing is used with manual intervention in the decision-making of recommendation systems. To understand the effectiveness of the recommendations, user interaction data is compared to compare experiences. Based on the comparisons, the experiments conclude the effectiveness of A/B testing for recommendation systems.