SIMILARITY MEASURES IN MOVIE RECOMMENDATION USING NLP TECHNIQUES

Kasturi, K and Jebathangam, J (2025) SIMILARITY MEASURES IN MOVIE RECOMMENDATION USING NLP TECHNIQUES. In: Artificial Intelligence for Better Tomorrow. Bhumi Publishing, India, pp. 59-75. ISBN 978-93-48620-72-9

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Abstract

The Movie Recommendation System is designed to help users discover movies based on
their preferences. This project explores the application of natural language processing (NLP)
techniques to recommend movies by analyzing their descriptions. Both CountVectorizer and TF-
IDF Vectorizer were employed separately to test which method offers better accuracy in
generating movie recommendations. The system processes and transforms movie descriptions
into numerical features that can be compared to provide relevant suggestions, ensuring the
recommendations are based on the most appropriate text features.

Item Type:	Book Section
Subjects:	Computer Applications > Artificial Intelligence
Domains:	Computer Applications
Depositing User:	IR Admin
Date Deposited:	02 Sep 2026 06:00
Last Modified:	02 Sep 2026 06:02
URI:	https://ir.vistas.ac.in/id/eprint/21706

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