Exploring the relationship between user’s characteristics and movie recommendations using a KNN-based recommender system

Research Article
Open access

Exploring the relationship between user’s characteristics and movie recommendations using a KNN-based recommender system

Muhao Hu 1* , Wufan Xiao 2 , Yuzhang Li 3
  • 1 University of Minnesota    
  • 2 University of Washington    
  • 3 Tianjin University of Science and Technology     
  • *corresponding author muhaohu1023@gmail.com
ACE Vol.44
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-327-2
ISBN (Online): 978-1-83558-328-9

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.

Keywords:

Movie Recommendation System, KNN algorithm, linear regression, Weight Based KNN Recommender System

Hu,M.;Xiao,W.;Li,Y. (2024). Exploring the relationship between user’s characteristics and movie recommendations using a KNN-based recommender system. Applied and Computational Engineering,44,114-123.
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References

[1]. Wang, B., Liao, Q., & Zhang, C. (2013, August). Weight based KNN recommender system. In 2013 5th International Conference on Intelligent Human-Machine Systems and cybernetics (Vol. 2, pp. 449-452). IEEE.

[2]. GroupLens. (1998). MovieLens 100K Dataset [Data set] https://grouplens.org/datasets/movielens/100k/

[3]. Subramaniyaswamy, V., & Logesh, R. (2017). Adaptive KNN based recommender system through mining of user preferences. Wireless Personal Communications, 97, 2229-2247.

[4]. Jahrer, M., Töscher, A., & Legenstein, R. (2010, July). Combining predictions for accurate recommender systems. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 693-702).

[5]. Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of translational medicine, 4(11).

[6]. H, R. S. (2023a, April 5). K-nearest neighbors algorithm. Intuitive Tutorials. https://intuitivetutorial.com/2023/04/07/k-nearest-neighbors-algorithm/

[7]. Bahrani, P., Minaei-Bidgoli, B., Parvin, H., Mirzarezaee, M., & Keshavarz, A. (2023). A new improved KNN-based recommender system. Theournal of Supercomputing, 1-35.

[8]. Jingwen, Z. (2017). R algorithm for MovieLens [Source code]. GitHub.https://github.com/jingwen-z/R/blob/master/algorithm/MovieLens.R

Cite this article

Hu,M.;Xiao,W.;Li,Y. (2024). Exploring the relationship between user’s characteristics and movie recommendations using a KNN-based recommender system. Applied and Computational Engineering,44,114-123.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-327-2(Print) / 978-1-83558-328-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.44
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Wang, B., Liao, Q., & Zhang, C. (2013, August). Weight based KNN recommender system. In 2013 5th International Conference on Intelligent Human-Machine Systems and cybernetics (Vol. 2, pp. 449-452). IEEE.

[2]. GroupLens. (1998). MovieLens 100K Dataset [Data set] https://grouplens.org/datasets/movielens/100k/

[3]. Subramaniyaswamy, V., & Logesh, R. (2017). Adaptive KNN based recommender system through mining of user preferences. Wireless Personal Communications, 97, 2229-2247.

[4]. Jahrer, M., Töscher, A., & Legenstein, R. (2010, July). Combining predictions for accurate recommender systems. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 693-702).

[5]. Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of translational medicine, 4(11).

[6]. H, R. S. (2023a, April 5). K-nearest neighbors algorithm. Intuitive Tutorials. https://intuitivetutorial.com/2023/04/07/k-nearest-neighbors-algorithm/

[7]. Bahrani, P., Minaei-Bidgoli, B., Parvin, H., Mirzarezaee, M., & Keshavarz, A. (2023). A new improved KNN-based recommender system. Theournal of Supercomputing, 1-35.

[8]. Jingwen, Z. (2017). R algorithm for MovieLens [Source code]. GitHub.https://github.com/jingwen-z/R/blob/master/algorithm/MovieLens.R

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