Santosh Saha
Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majhitar, Sikkim Manipal University, 737136, Gangtok, Sikkim, India.

Amrita Biswas
Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majhitar, Sikkim Manipal University, 737136, Gangtok, Sikkim, India.

Saumya Das
Department of Computer Science and Engineering – AI, Brainware University, 700125, Barasat, Kolkata, India.

Nitai Paitya
Amity Institute of Information Technology, Amity University, 700135, Kolkata, India.

DOI https://doi.org/10.33889/IJMEMS.2026.11.1.019

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Abstract

A recommendation system (RS) leverages machine learning by analyzing user behavior, and suggesting relevant products based on the user's preferences. Long-tail items, which were once leading products in their niche, became harder to find and newer items are heavily promoted to users, long-tail items, which can boost customer engagement and ensure that long-tail items remain visible. In this paper, we have provided extensive efforts to conduct a systematic review of the long-tail recommendation system, based on PRISMA 2020 guidelines. Studies published between 2012 and 2024 were identified, through a detailed search in ACM Digital Library, Science Direct, SpringerLink, IEEE Xplore, and Google Scholar. We conducted a detailed investigation into a long-tail recommendation system which focused on finding different categories, datasets, and evaluation metrics. This literature review provides an extensive overview of the selection of datasets, different categories of long-tail recommendation, and evaluation criteria for the researchers and individuals who are new to the domain of long-tail recommendation systems.

Keywords- Recommendation system, Long-tail, Long-tail recommender system, Artificial intelligence, Systematic literature review

Citation

Saha, S., Biswas, A., Das, S., & Paitya, N. (2026). Evaluating the Long Tail in Recommendation System: A Systematic Review of Approaches, Datasets and Metrics. International Journal of Mathematical, Engineering and Management Sciences, 11(1), 439-471. https://doi.org/10.33889/IJMEMS.2026.11.1.019.