Research on Personalized Movie Recommendation System Based on Collaborative Filtering

Zijie Sheng

doi:10.54097/hhfgh748

Authors

Zijie Sheng

DOI:

https://doi.org/10.54097/hhfgh748

Keywords:

Collaborative Filtering; Matrix Factorization; Movie Recommendation System; Personalized Recommendation.

Abstract

It is becoming increasingly apparent that the wealth of Internet movie resources can, on occasion, be overwhelming. This has brought to the fore the importance of personalized movie recommendation systems. This paper explores such systems based on collaborative filtering. The MovieLens100k dataset is selected for analysis, and its source, scale, characteristics, user ratings, movies, and user information it contains are described in detail. We then constructed a collaborative filtering recommendation model and analyzed and compared the performance of different algorithms. The paper goes on to propose a hybrid collaborative filtering approach with the aim of achieving a comprehensive system with good scalability and maintainability. Finally, we hope that the paper will be of interest to readers and that it may even encourage further discussion and research in this area, for example in the integration of deep learning and big data processing, with a view to improving the accuracy and intelligence of movie recommendations and enhancing the user viewing experience.

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