Analysing Viewer Engagement and Preferences in Anime Streaming Platforms

Ayyalusamy, Subash (2023) Analysing Viewer Engagement and Preferences in Anime Streaming Platforms. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study examines anime recommendation systems, including collaborative filtering embedded with neural networks and content-based filtering enhanced with the K-Nearest Neighbours algorithm. Jupyter Notebook simplifies the process of model training and validation systematically. The evaluation process focuses on assessing the RMSE scores, training outcomes, and visual representations, which provide user preferences and top recommendations. The studies are divided into categories, including the state of the anime, the distribution of user ratings, and the RMSE values of content-based filtering. An analysis of the advantages of collaborative filtering versus content-based filtering is conducted, resulting in the development of a hybrid model for recommendation. The study promotes the importance of optimizing algorithms for efficiency, exploring different domains, and providing real-time recommendations. It highlights the need to include measures other than Root Mean Square Error (RMSE) to measure user engagement. The addition of comprehensive research references and GitHub links for code access enhances the overall comprehension of recommendation systems.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Menghwar, Teerath Kumar UNSPECIFIED
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	06 May 2025 18:24
Last Modified:	06 May 2025 18:24
URI:	https://norma.ncirl.ie/id/eprint/7493

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