A

Project Synopsis
on

“Movie Recommendation System”

Submitted in partial fulfilment for the award of degree of

BACHELOR OF TECHNOLOGY
in
COMPUTER SCIENCE AND ENGINEERING

Submitted By:
Tushar Sharma [21EGJCS158]
Vinay Kumar Jha[21EGJAD027]
Justin tirkey [21EGJCS060]

DEPARTMENT OF COMPUTER SCIENCE ENGINEERING

GLOBAL INSTITUTE OF TECHNOLOGY
JAIPUR (RAJASTHAN)-302022
Session 2024-25
Introduction

Movie recommendation systems help users discover movies based

on their preferences. These systems use various algorithms to
predict what a user might like based on their history or preferences.

Recommendation systems are commonly used in platforms like

Netflix, Amazon Prime, and Hulu.
Types of Recommendation Systems

▶ Collaborative Filtering: Based on user behavior and ratings.

▶ Content-Based Filtering: Recommends based on movie
features (e.g., genre, director).
▶ Hybrid Approach: Combines both methods to improve
accuracy.
Collaborative Filtering

Collaborative Filtering is based on the idea that users who liked

similar movies in the past will like similar movies in the future.
This method can be:
▶ User-Based: Finding users similar to the current user and
recommending items they liked.
▶ Item-Based: Finding items similar to those the user liked and
recommending those items.
Content-Based Filtering

Content-Based Filtering recommends movies based on the features

of movies the user has previously liked, such as:
▶ Genre
▶ Director
▶ Actors
▶ Keywords in the plot
This method ensures that movies with similar content to what the
user liked are recommended.
Hybrid Recommendation System

A Hybrid system combines both Collaborative and Content-Based

filtering to enhance the accuracy of recommendations. It can
overcome the limitations of both approaches and improve
prediction quality.
Challenges in Recommendation Systems

▶ Cold Start Problem: Difficulty recommending to new users

or with limited data.
▶ Scalability: Handling a large amount of data and users.
▶ Bias: Systems may be biased towards certain genres or types
of movies.
Data Collection

Data for movie recommendations can come from:

▶ User movie ratings (e.g., 1-5 stars).
▶ Movie metadata (e.g., genre, director, actors).
▶ Social media activity (e.g., likes, shares, and reviews).
This data is used to build the recommendation model.
Building the Recommendation Model

The process includes:

▶ Collecting data from various sources.
▶ Preprocessing and cleaning the data.
▶ Implementing collaborative or content-based algorithms.
▶ Evaluating the model’s performance using metrics like
precision, recall, and F1-score.
Popular Algorithms

▶ K-Nearest Neighbors (KNN): Used for both collaborative

and content-based filtering.
▶ Matrix Factorization: Decomposes the user-item interaction
matrix into lower-dimensional matrices, typically using
Singular Value Decomposition (SVD).
▶ Neural Networks: Advanced deep learning algorithms used
for better accuracy and handling complex relationships.
Evaluation Metrics

Common evaluation metrics for recommendation systems include:

▶ Precision and Recall: Measures the relevance of
recommended items.
▶ Mean Absolute Error (MAE): Measures the accuracy of
predicted ratings.
▶ F1-Score: Harmonic mean of precision and recall, used for
balancing both.
Example of Movie Recommendation System

▶ User A likes movies in the “Action” and “Sci-Fi” genres.

▶ The system suggests movies with similar genres, ratings, and
actors.
For example, if User A liked ”The Matrix,” the system might
recommend ”Inception” or ”Interstellar.”
Real-World Applications

▶ Netflix: Uses collaborative filtering to suggest movies based

on your watch history.
▶ Amazon Prime: Recommends based on both content and
user preferences.
▶ Spotify: Uses a hybrid model to recommend music based on
listening habits.
Future of Recommendation Systems

With advancements in machine learning and AI, future

recommendation systems will:
▶ Provide hyper-personalized recommendations.
▶ Incorporate more data sources like social media activity,
browsing history, and even real-time events.
▶ Use advanced deep learning models for more accurate and
diverse recommendations.
Conclusion

Movie recommendation systems are a vital component in delivering

personalized experiences to users. Although there are challenges
such as the cold start problem and bias, the continuous
development of algorithms and the use of hybrid approaches are
helping overcome these obstacles.

Thanks for your attention!

Movie Recommendation System

Your Name

December 7, 2024
Introduction

Movie recommendation systems help users discover movies based

on their preferences. These systems use various algorithms to
predict what a user might like based on their history or preferences.

Recommendation systems are commonly used in platforms like

Netflix, Amazon Prime, and Hulu.
Types of Recommendation Systems

▶ Collaborative Filtering: Based on user behavior and ratings.

▶ Content-Based Filtering: Recommends based on movie
features (e.g., genre, director).
▶ Hybrid Approach: Combines both methods to improve
accuracy.
Collaborative Filtering

Collaborative Filtering is based on the idea that users who liked

similar movies in the past will like similar movies in the future.
This method can be:
▶ User-Based: Finding users similar to the current user and
recommending items they liked.
▶ Item-Based: Finding items similar to those the user liked and
recommending those items.
Content-Based Filtering

Content-Based Filtering recommends movies based on the features

of movies the user has previously liked, such as:
▶ Genre
▶ Director
▶ Actors
▶ Keywords in the plot
This method ensures that movies with similar content to what the
user liked are recommended.
Hybrid Recommendation System

A Hybrid system combines both Collaborative and Content-Based

filtering to enhance the accuracy of recommendations. It can
overcome the limitations of both approaches and improve
prediction quality.
Challenges in Recommendation Systems

▶ Cold Start Problem: Difficulty recommending to new users

or with limited data.
▶ Scalability: Handling a large amount of data and users.
▶ Bias: Systems may be biased towards certain genres or types
of movies.
Data Collection

Data for movie recommendations can come from:

▶ User movie ratings (e.g., 1-5 stars).
▶ Movie metadata (e.g., genre, director, actors).
▶ Social media activity (e.g., likes, shares, and reviews).
This data is used to build the recommendation model.
Building the Recommendation Model

The process includes:

▶ Collecting data from various sources.
▶ Preprocessing and cleaning the data.
▶ Implementing collaborative or content-based algorithms.
▶ Evaluating the model’s performance using metrics like
precision, recall, and F1-score.
Popular Algorithms

▶ K-Nearest Neighbors (KNN): Used for both collaborative

and content-based filtering.
▶ Matrix Factorization: Decomposes the user-item interaction
matrix into lower-dimensional matrices, typically using
Singular Value Decomposition (SVD).
▶ Neural Networks: Advanced deep learning algorithms used
for better accuracy and handling complex relationships.
Evaluation Metrics

Common evaluation metrics for recommendation systems include:

▶ Precision and Recall: Measures the relevance of
recommended items.
▶ Mean Absolute Error (MAE): Measures the accuracy of
predicted ratings.
▶ F1-Score: Harmonic mean of precision and recall, used for
balancing both.
Example of Movie Recommendation System

▶ User A likes movies in the “Action” and “Sci-Fi” genres.

▶ The system suggests movies with similar genres, ratings, and
actors.
For example, if User A liked ”The Matrix,” the system might
recommend ”Inception” or ”Interstellar.”
Real-World Applications

▶ Netflix: Uses collaborative filtering to suggest movies based

on your watch history.
▶ Amazon Prime: Recommends based on both content and
user preferences.
▶ Spotify: Uses a hybrid model to recommend music based on
listening habits.
Future of Recommendation Systems

With advancements in machine learning and AI, future

recommendation systems will:
▶ Provide hyper-personalized recommendations.
▶ Incorporate more data sources like social media activity,
browsing history, and even real-time events.
▶ Use advanced deep learning models for more accurate and
diverse recommendations.
Conclusion

Movie recommendation systems are a vital component in delivering

personalized experiences to users. Although there are challenges
such as the cold start problem and bias, the continuous
development of algorithms and the use of hybrid approaches are
helping overcome these obstacles.

Thanks for your attention!