MOVIE RECOMMENDATION SYSTEM

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Dr.R.S.Karthik; Mr S.Ponvishnu; Mr B.Nithish Kumar

MOVIE RECOMMENDATION SYSTEM

Authors: Dr.R.S.Karthik, Mr S.Ponvishnu, Mr B.Nithish Kumar

Unique Paper ID: 174725
Volume: 11
Issue: 11
PageNo: 1309-1311

Keywords: Movie Recommendation System Content-Based Filtering Cosine Similarity TMDB API Machine Learning Flask

Abstract:
In today’s world of vast entertainment content, finding the right movie to watch can be overwhelming. Recommender systems have become an essential tool to solve this problem, guiding users by suggesting movies they are likely to enjoy based on their previous interests or content similarity. This project leverages content-based filtering and cosine similarity to analyze movie features and suggest the most relevant recommendations. This movie recommendation system allows users to find the movies in the website called CinePulse, it takes them to the movie page and the users can find the details of that movie such as rating, cast, release date, genre etc., whatever the data available from IMDB website holds about the movie. The main part of this project, it recommends similar movies based on content-based filtering and cosine similarity. Cosine similarity measures how similar two vectors are by calculating the cosine of the angle between them. Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback. These are used to identify the value of similarity between the movies which are then used to recommend the users below.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 1309-1311

MOVIE RECOMMENDATION SYSTEM

Available:https://ijirt.org/article?manuscript=174725

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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