Building a Movie Recommendation System Using Neo4j Graph Database: A Case Study of Netflix Movie Dataset

Awliya Hanun Izdihar, Nazriyah Deny Tsaniyah, Faraz Nurdini, Belva Rizki Mufidah, Nur Aini Rakhmawati*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recommendations for movies can help discover new and enjoyable movies. This study uses the Neo4j Graph Database to create a recommendation system using the Netflix Movie Dataset. The objective of this research is the development of a movie recommendation algorithm using the k-NN similarity algorithm and FastRP node embedding machine learning. The results have provided recommendations based on similar attributes, such as actors, directors, country, type, and rating. In this research, we used a similarity scale of 0-1 so that we found 200 pairs of movies that were similar between each other and there were 62070 movies that were less similar between each other.

Original languageEnglish
Title of host publication2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages614-618
Number of pages5
ISBN (Electronic)9798350372229
DOIs
Publication statusPublished - 2024
Event2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024 - Manama, Bahrain
Duration: 28 Jan 202429 Jan 2024

Publication series

Name2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

Conference

Conference2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024
Country/TerritoryBahrain
CityManama
Period28/01/2429/01/24

Keywords

  • FastRP
  • Neo4j
  • graph
  • k-NN
  • movies
  • recommendation system

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