Automotive Movie Recommendation System based on Natural Language Processing

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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5.6617
Keywords:
Cosine similarity, TfidfTransformer, count vectorizer, scikit-learn, Kaggle

Main Article Content

Pooja Bagane
Department of CSE, Symbiosis Institute of Technology, Constituent of Symbiosis International (Deemed University), Pune, India
Sudhanshu Gonge
Department of CSE, Symbiosis Institute of Technology, Constituent of Symbiosis International (Deemed University), Pune, India
Rahul Joshi
Department of CSE, Symbiosis Institute of Technology, Constituent of Symbiosis International (Deemed University), Pune, India
Obsa Amenu Jebessa
Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma, Oromia, Ethiopia

Abstract

People are puzzled about which movie to watch these days because there are so many movies available on various OTT platforms. A recommender system would solve this problem by recommending the best movie to the user based on his genre, actor, director, and rating preferences. The cosine similarity principle would be used to guide the recommendation system. Apart from that, we will use the Tfidftransformer and count vectorizer from the sci-kit-learn library in Python in this work. In this study work, all of the approaches' constraints have been described. All of this work was done using datasets from several OTT platforms that were available on Kaggle.

Article Details

How to Cite
Bagane, P. ., Gonge, S. ., Joshi, R. ., & Jebessa, O. A. . (2023). Automotive Movie Recommendation System based on Natural Language Processing . International Journal on Recent and Innovation Trends in Computing and Communication, 11(5), 300–303. https://doi.org/10.17762/ijritcc.v11i5.6617
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Vol. 11 No. 5 (2023): May (2023) Issue
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Articles

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