An accurate similarity-based model for movie rating prediction and recommendation using an uncertainty score

Abstract

This paper presents a novel method for movie rating prediction and recommendation systems based on similarity between movies with an uncertainty score to control prediction confidence. The two traditional recommendation approaches, namely collaborative filtering and content-based, rely on the concept of similarity between movies and users. Although similarity plays a crucial role in recommendation systems, it has not been sufficiently explored in existing research. To bridge this gap, we propose a dissimilarity function for movies based on a thorough analysis of movie features. We also introduce an uncertainty score that quantifies the confidence in predictions based on the dissimilarity between the unseen movie and the nearest rated movie. The proposed method uses the uncertainty score for two purposes. First, it adjusts the predicted rating by shifting it toward the user’s mean rating when the uncertainty exceeds a predefined threshold. Second, it prioritizes recommendations based on the uncertainty score, allowing the system to recommend only movies with high prediction certainty. The experimental results show that the proposed method is significantly accurate at lower uncertainty thresholds (≤12%). Furthermore, the method also performs well in top-K movie recommendations, providing consistent performance regardless of the number of recommended movies when uncertainty is low. The proposed method is also compared with state-of-the-art machine learning models, such as Support Vector Machine Regression, Random Forest Regressor, and Gradient Boosting Regressor. The comparison shows that our approach outperforms these models at low uncertainty levels and provides more reliable and accurate recommendations.