Movie Recommendation System and Data Analysis

We are living in a digital era, nowadays everything starting from shopping to entertainment is easily accessible online to all the age groups, thanks to the user-friendly interface. This technological advancement was maximized during COVID-19 and continues to flourish till now. But this development made about 29.1 percent of the population that consists of children under the age of 18 vulnerable to movies, shows having illegal and adult content. Not only this, even for adults it can be a tiresome process to scroll through a long list of movies on sites just to find that one movie of your taste. In such cases, a recommendation system will help the user filter out the content easily. In this paper, we have done detailed data analysis over the IMDB movie dataset and created a recommendation system where users can look for movies on the basis of actor, director, genre, language etc. This system works using the concept of correlation and feature engineering.

• Publication year

  2023

• Venue

  2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3)

• Publication date

  2023-06-08

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/IC2E357697.2023.10262669
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Research on Cosine Similarity and Pearson Correlation Based Recommendation Models

  2021cited by this paper
• Movie Recommendation System using Cosine Similarity and KNN

  2020cited by this paper
• A review of movie recommendation system: Limitations, Survey and Challenges

  2020cited by this paper
• Content-Based Movie Recommendation System Using Genre Correlation

  2019cited by this paper
• Movie Recommendation System Using Collaborative Filtering

  2018cited by this paper
• A Hybrid Approach for Movie Recommendation System Using Feature Engineering

  2018cited by this paper
• A Neural Engine for Movie Recommendation System

  2018cited by this paper
• A Survey on Recommendation System

  2017cited by this paper
• A personalised movie recommendation system based on collaborative filtering

  2017cited by this paper
• Data Cleaning: Overview and Emerging Challenges

  2016cited by this paper
• A Movie Recommender System: MOVREC

  2015cited by this paper
• Comparative recommender system evaluation: benchmarking recommendation frameworks

  2014cited by this paper
• Incorporating group recommendations to recommender systems: Alternatives and performance

  2013cited by this paper
• A movie recommendation algorithm based on genre correlations

  2012cited by this paper
• A Comprehensive Survey of Neighborhood-based Recommendation Methods

  2011cited by this paper
• Using contextual information and multidimensional approach for recommendation

  2009cited by this paper
• Personalization of Supermarket Product Recommendations

  2004cited by this paper
• Hybrid Recommender Systems: Survey and Experiments

  2002cited by this paper
• Item-based collaborative filtering recommendation algorithms

  2001cited by this paper
• Statistical Data Fusion for Cross-Tabulation

  1997cited by this paper
• User Modeling and User-Adapted Interaction

  1996cited by this paper
• Social information filtering: algorithms for automating “word of mouth”

  1995cited by this paper
• Recommending and evaluating choices in a virtual community of use

  1995cited by this paper

• No citing papers are available for this paper.