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.
|
Text
WURO_2024 Systematic Review of Machine.pdf - Published Version Available under License Creative Commons Attribution. Download (509kB) | Preview |
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 |
Actions (login required)
 |
Edit Item |