Speaker
Description
This project presents the development of a movie recommendation system implemented using the R programming language. The primary objective of the project is to design a data-driven model capable of suggesting relevant movies to users based on patterns identified in historical rating data. R was chosen for this project due to its strong capabilities in statistical computing, data analysis, and visualization.
The contribution of R in this project begins with data preprocessing and exploration. Large movie rating datasets were imported, cleaned, and structured using R libraries such as dplyr, tidyr, and readr. Missing values, inconsistent entries, and redundant data were handled to ensure reliable input for the recommendation model. Exploratory data analysis was conducted using ggplot2 to identify user rating trends, popular genres, and distribution patterns within the dataset.
R was also used to implement the core recommendation algorithm. Collaborative filtering techniques were applied using packages such as recommenderlab, enabling the system to identify similarities between users and movies. By constructing a user–item rating matrix, the model predicts unseen ratings and generates personalized movie recommendations. Matrix operations and similarity measures such as cosine similarity were utilized to improve recommendation accuracy.
Furthermore, R facilitated model evaluation and performance analysis. Metrics such as precision, recall, and prediction accuracy were computed to assess the effectiveness of the recommendation system. Visualization tools were used to interpret model performance and recommendation quality.
Overall, this project demonstrates how R can be effectively utilized for building a complete movie recommendation pipeline, including data preprocessing, model development, evaluation, and visualization. The use of R enables efficient handling of large datasets and provides a flexible environment for developing scalable recommendation systems.
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Gemini Pro for additional help during the project
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | statistical learning, movie recommendation, statistical model, collaborative filtering, data analysis |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |