A Human Disposition Based Movie Recommendation System |
Author(s): |
| Radhika Dherge , GES R. H. Sapat College of Engineering; Nilesh Alone, GES R. H. Sapat College of Engineering; Nikhil Chaudhari, GES R. H. Sapat College of Engineering; Srushti Chaudhry, GES R. H. Sapat College of Engineering; Ayush Gaikwad, GES R. H. Sapat College of Engineering |
Keywords: |
| Human Disposition-Based Recommendation System, Personalized Movie Recommendations, Singular Value Decomposition (SVD), K-Nearest Neighbors (KNN), Meta-Learning, Reinforcement Learning |
Abstract |
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Traditional movie recommendation systems often struggle with limitations such as the cold start problem, static algorithms, and reliance on explicit feedback, leading to suboptimal personalization. This project proposes a human disposition-based recommendation system that integrates Singular Value Decomposition (SVD), K-Nearest Neighbors (KNN), meta-learning, and reinforcement learning to dynamically adapt to user preferences. By incorporating emotional and behavioral factors, the system enhances personalization and responsiveness. Performance evaluations demonstrate significant improvements, with an accuracy of ~85% and reduced computational complexity, surpassing traditional systems' typical accuracy of 70–75%. This approach promises a more engaging and efficient recommendation experience. |
Other Details |
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Paper ID: IJSRDV13I30098 Published in: Volume : 13, Issue : 3 Publication Date: 01/06/2025 Page(s): 132-134 |
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