Exploring the relationship between users characteristics and movie recommendations using a KNN-based recommender system

Abstract

The central aim of this research is to elucidate the degree to which demographic variables, including but not limited to age and gender, bear on the performance of movie recommendation algorithms within film recommendation systems. Further, we endeavor to uncover any existent correlations between these user characteristics and the resultant outputs of such systems. Leveraging the expansive dataset available via MovieLens, we employ a linear regression model to ascertain the four critical variables (age, gender, occupation, and the average user rating for films previously watched) that have the most profound influence on movie recommendation algorithms. Once these salient factors have been determined, we assign their respective weights and incorporate these into a KNN algorithm. We then subject the resultant model to rigorous testing to verify the accuracy of our results and to ascertain whether the integration of these weighted elements enhances the overall precision of the movie recommendation system. While extant literature predominantly focuses on the amalgamation of KNN with other algorithms, our study charts a novel course by using linear regression. This methodology allows us to intuitively illustrate the relationship between user demographics and the movie recommendation system and enables us to evaluate whether emphasizing certain characteristics can augment the system's effectiveness. Our findings suggest that of all the user characteristics examined, the mean of users ratings for movies previously watched exerts the greatest influence on the outputs of the movie recommendation system. Moreover, incorporating weights reflective of the average user ratings across all movie features within the KNN algorithm can significantly bolster the accuracy of the resultant movie recommendations.