| dc.description.abstract | With the rapid development of OTT platforms, users face the challenge of information overload, requiring additional time to select and analyze the desired content. Furthermore, many OTT platforms adopt subscription-based models, making the accuracy and diversity of recommendation systems crucial for maintaining user interest in the platform. In the field of recommendation systems, hybrid filtering is widely used to address the issues arising from employing a single recommendation algorithm. However, the majority of current research focuses on the accuracy of recommendation systems, with relatively less exploration on enhancing diversity. Therefore, this study combines two different recommendation algorithms, focusing on the application of movie recommendations on OTT platforms. Building upon collaborative filtering as the foundation, content-based filtering is used to extend the existing recommendation list, comparing the effects of different hybrid approaches on the accuracy and diversity of the recommendation system. The objective of this study is to explore the efficacy of enhancing the diversity in recommendations generated by content-based filtering by incorporating collaborative filtering techniques. Ultimately, this study propose a recommendation system that achieves a balance between accuracy and diversity, with broad applicability, to enhance the user experience and loyalty on OTT platforms. | en_US |