Deep Anime Recommendation System: Recommending Anime Using Hybrid Filtering

Subburaj, Varshini (2024) Deep Anime Recommendation System: Recommending Anime Using Hybrid Filtering. Masters thesis, Dublin, National College of Ireland.

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Abstract

The Deep Anime Recommendation System employing Hybrid Filtering is a groundbreaking approach in the growing field of online anime streaming. Traversing a huge expanse of anime titles presents difficulties such as mental exhaustion from making choices and overlooking potential content chances. Conventional recommendation systems often fail to adjust to subtle user preferences, necessitating the employment of a new technique. The study presents a hybrid filtering approach that combines content-based and collaborative filtering strategies, bolstered by deep learning capabilities. The technology tries to revolutionise the experience of discovering anime by simultaneously examining content characteristics and user preferences. Beyond the constraints of traditional systems, it enhances recommendation processes by providing customised ideas that are specifically tuned to individual preferences. This research not only enhances the development of anime recommendation systems but also presents a hybrid recommendation system that integrates content-based and collaborative filtering approaches with deep learning to provide more precise and personalised anime suggestions. The hybrid system surpasses the performance of basic models, clearly showing its efficacy in improving user engagement and contentment in the ever-changing world of anime streaming services. The suggested Deep Anime Recommendation System is a technological innovation that has the potential to transform personalised content discovery on internet streaming platforms.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham 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 Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Film Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	05 Jun 2025 13:48
Last Modified:	05 Jun 2025 13:48
URI:	https://norma.ncirl.ie/id/eprint/7762

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