Bhat, S.S.Pranav, P.Shashank, K.V.Raghunandan, A.Mohan, B.R.2026-02-0620222022 6th International Conference on Trends in Electronics and Informatics, ICOEI 2022 - Proceedings, 2022, Vol., , p. 985-991https://doi.org/10.1109/ICOEI53556.2022.9777116https://idr.nitk.ac.in/handle/123456789/29967In today's world, recommendation algorithms are popularly utilised for personalization. To improve their business, e-commerce behemoths rely heavily on their recommendation algorithms. As a result, the quality of suggestions can have a big impact on how much money they make. As a result, effective evaluation of recommender systems is critical. Traditional evaluation measures are limited to error-based and accuracy-based metrics, and do not account for characteristics such as novelty, informedness, markedness, and so on. This research study aims to compare the effectiveness of two web-based book recommendation systems by using the measures like diversity, informedness, and markedness, which are less well-known but equally essential. © 2022 IEEE.diversityinformednessmarkednessmetricsperformance testingprecisionrecallrecommendation systemsROC