Movie Recommender System

The goal of this project is to develop a Film Tip Engine utilizing the power of Expert-System, Deep Learning, Computational Linguistics (CLs). The system utilizes Python's programming capabilities to cater to the varied cinematic preferences of users. The core objective is to curate a personalized movie-watching experience by analyzing user preferences and past interactions. Drawing inspiration from the success of streaming services like Netflix and Amazon Prime, the system aims to revolutionize the way users discover and enjoy films. It employs advanced algorithms to interpret user data and provide tailored suggestions, thereby simplifying the selection process. The recommendation engine is built upon a foundation of Social Filtering and Based-Profile Filtering, integrated with Deep Learning. This integration leads to more accurate recommendations.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/i3ceet61722.2024.10993868
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
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article

Movie Recommender System

Shubham Shekhar, Sunny Sharma, Sushma Pandey, Rajiv Kumar Nath
Customer churn and segmentation
article

Movie Recommender System

Shubham Shekhar, Sunny Sharma, Sushma Pandey, Rajiv Kumar Nath
article en

Abstract

The goal of this project is to develop a Film Tip Engine utilizing the power of Expert-System, Deep Learning, Computational Linguistics (CLs). The system utilizes Python's programming capabilities to cater to the varied cinematic preferences of users. The core objective is to curate a personalized movie-watching experience by analyzing user preferences and past interactions. Drawing inspiration from the success of streaming services like Netflix and Amazon Prime, the system aims to revolutionize the way users discover and enjoy films. It employs advanced algorithms to interpret user data and provide tailored suggestions, thereby simplifying the selection process. The recommendation engine is built upon a foundation of Social Filtering and Based-Profile Filtering, integrated with Deep Learning. This integration leads to more accurate recommendations.

Sharda University (IN)
Openalex Percentile: Top 57%
Customer churn and segmentation
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