| Autor: |
Prema, S., Varalatchoumy, M., Nirmaladevi, G., Vijayakumar, S., Kayalvili, S., Rajendiran, M., Premanand, R., Vijayan, V. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3193 Issue 1, p1-11, 11p |
| Abstrakt: |
Through the use of interest models, the Recommender System assists users in discovering content that is relevant to them. In order to make product suggestions based on past purchases, content-based recommender systems do not require user ratings. These systems are the subject of this study. Now these systems can profile products and customers using machine learning. Together with Non-Negative Matrix Factorization Clustering (NNMFC), we present a new approach to collaborative learning for online video sites. The research utilizes a sliding window clustering approach that relies on Particle Swarm Optimization (PSO) and gradient descent. We utilized three approaches to assess the model's performance: sliding window PSO (SWPSO), sliding window gradient descent and gradient descent. The Root Mean Square Error (RMSE) was calculated for each. Outperforming current state-of-the-art methods like UPCSim, K-Mean, and Collaborative Filtering, the suggested work's result analysis attained the lowest RMSE of 1.02. With a significant improvement of 10.2% over previous techniques, the Sliding Window PSO was shown to be the most effective. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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