Classifications, evaluation metrics, datasets, and domains in recommendation services: A survey

Article type: Research Article

Authors: Vuong Nguyen, Luong

Affiliations: Department of Artificial Intelligence, FPT University Da Nang, Da Nang, Vietnam | E-mail: vuongnl3@fe.edu.vn

Correspondence: [*] Corresponding author: Department of Artificial Intelligence, FPT University Da Nang, Da Nang, Vietnam. E-mail: vuongnl3@fe.edu.vn.

Abstract: Recommendation systems (RS) play a crucial role in assisting individuals in making suitable selections from an extensive array of products or services. This significantly mitigates the predicament of being overwhelmed by excessive information. RS finds powerful utility in online industries by vending products over the internet or furnishing online services. Given the potential for business expansion through their implementation, RS is relevant in such domains. This comprehensive review article overviews RS and its diverse variations and extensions. Specifically, this review provides a thorough comparative analysis for each method that encompasses many techniques employed in RS, encompassing content-based filtering, collaborative filtering, hybrid, and miscellaneous approaches. Notably, the article delves into the manifold applications of RS across various practical domains. Additionally, the assortment of evaluation metrics utilized across RS is explored. Finally, we conclude by encapsulating the distinct challenges RS encounters, which enhance their precision and dependability.

Keywords: Recommendation systems, collaborative filtering, cold-start

DOI: 10.3233/HIS-240003

Journal: International Journal of Hybrid Intelligent Systems, vol. 20, no. 2, pp. 85-100, 2024