Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade

Weiner, Felix, Teh, Phoey Lee and Cheng, Chi-Bin (2024) Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade. In: Intelligent Computing, SAI 2024, 26-27 June 2024, London, United Kingdom.

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Abstract

This study presents a comprehensive overview of the approaches employed in recommendation systems over the last decade. The review primarily draws from two categories of filtering techniques: content-based filtering and collaborative filtering methods. We have reviewed and tabulated approximately forty articles that have been published. Major findings include: (1) collaborative filtering is more often used than content-based filtering, 70% to 23%, the rest is hybrid methods of these two; (2) more than half of the machine learning approaches adopted are supervised learning; however, (3) algorithm-wise, K-means the unsupervised learning algorithm emerged as the most frequently adopted approach in recommendation systems. Also notably, cosine similarity stands out as the prevalent measurement technique.

Item Type: Conference or Workshop Item (Paper)
Keywords: Artificial Intelligent, Chat GPT, Human generated-text, AI generated text
Divisions: Applied Science, Computing and Engineering
Depositing User: Hayley Dennis
Date Deposited: 10 Jul 2024 13:04
Last Modified: 10 Jul 2024 13:22
URI: https://wrexham.repository.guildhe.ac.uk/id/eprint/18180

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