A comparative analysis on the different Recommendation Engines on the movies Dataset

Purushothama, Madhusudhan (2024) A comparative analysis on the different Recommendation Engines on the movies Dataset. Masters thesis, Dublin, National College of Ireland.

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Abstract

There has been an increase in volume of information due to the increase in growth of internet and easily accessible mobile devices and businesses that depend on the internet. This has led to the need to create a system than has the capacity to filter out important information for users. To solve for this issue a recommendation system can be used to provide the consumers the necessary information about the items or services based only on the requirement of the individual. A lot of research has been made on recommender systems and have developed various filtering algorithms that can increase the efficiency and effectiveness for the users and the system. In this work, I have conducted a comprehensive analysis of recommendation engines, evaluating them using the identical MovieLens dataset. This study involves comparing several distance approaches and norms for implementing recommender filtering systems. In this abstract, we aim to analyze various recommendation engines, including collaborative filtering, content-based filtering, machine learning models such as and k-NN, and deep learning models like LSTM and Siamese Networks and Sentence Transformers. The aim is to proceed with building a multimodal recommendation system to recommend movies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Uncontrolled Keywords:	Recommendation Engines; Similarity Check; Cosine Similarity; Collaborative Filtering; Content-Based Filtering; Hybrid Filtering; Classification Techniques; K Nearest Neighbours; L2 Norm; L1 Norm; L-p Norm
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Film Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	02 Jul 2025 17:41
Last Modified:	02 Jul 2025 17:41
URI:	https://norma.ncirl.ie/id/eprint/7998

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