Comparative Performance Evaluation of Web-Based Book Recommender Systems

Abstract

In 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.