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Conference object . 2023
License: CC BY
Data sources: ZENODO
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Article . 2023
License: CC BY
Data sources: Datacite
ZENODO
Article . 2023
License: CC BY
Data sources: Datacite
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Movie Recommendation using Content Based Filtering

Authors: Alan Shijo; Jetty Benjamin;

Movie Recommendation using Content Based Filtering

Abstract

Abstract— We propose a recommendation system based on machine learning that recommends movies to users based on movie metadata. The purpose of movie recommendation systems is to help movie viewers by suggesting films to watch without making them go through the difficult and time-consuming process of selecting from a large selection of films that number in the thousands or millions. The system takes an input movie and returns the top 5 recommendations based on the similarity of features such as genres, director, and cast. The system employs a simple similarity metric to compare the input movie with the recommended movies.

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Keywords

The Movie Database (TMDB) API, content-based filtering, similarity scores, Movie recommendation, movie details

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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