@article{akhadam_comparative_2025,
	title = {A {Comparative} {Evaluation} of {Recommender} {Systems} {Tools}},
	volume = {13},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/abstract/document/10879478},
	doi = {10.1109/ACCESS.2025.3541014},
	abstract = {Due to the vast flow of information on the Internet, easy and effective access to information has become crucial. Recommender systems are important in information filtering, as they significantly impact large-scale internet web services such as YouTube, Netflix, and Amazon. As the demand for personalized recommendations continues to grow, researchers and practitioners alike strive to develop tools specifically designed for this purpose to meet the increasing need. In this work, we address the challenges associated with selecting software frameworks and Machine Learning (ML) algorithms for Recommender Systems (RSs), thus, we offer a detailed comparison of 42 open-source RS software to provide insights into their different features and capabilities. Furthermore, the paper presents a concise overview of various ML algorithms to generate recommendations, reviews the most used performance metrics to evaluate RS, and then compares several ML algorithms provided by four popular recommendation tools: Microsoft Recommenders, Lenskit, Turi Create, and Cornac.},
	urldate = {2025-04-15},
	journal = {IEEE Access},
	author = {Akhadam, Ayoub and Kbibchi, Oumayma and Mekouar, Loubna and Iraqi, Youssef},
	year = {2025},
	pages = {29493--29522},
}

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