Movie Recommendation System – IT and Computer Engineering Guide
1. Project Overview
Objective: Build a system that recommends movies to users
based on their preferences or viewing history.
Scope: Use content-based filtering, collaborative filtering, or a hybrid
approach to generate personalized movie recommendations.
2. Prerequisites
Knowledge: Understanding of Python programming,
recommendation algorithms, and data preprocessing techniques.
Tools: Python, Jupyter Notebook, Pandas, NumPy, Scikit-learn, Matplotlib,
Seaborn, and Surprise library for collaborative filtering.
Dataset: MovieLens dataset or any other publicly available movie dataset.
3. Project Workflow
- Data Collection: Obtain a movie dataset like MovieLens.
- Data Preprocessing: Clean the data, handle missing values, and normalize features.
- Exploratory Data Analysis (EDA): Analyze user ratings, genres, and other attributes to identify patterns.
- Model Development: Implement content-based filtering using cosine similarity or collaborative filtering using matrix factorization.
- Model Evaluation: Use metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to evaluate recommendations.
- Optimization: Experiment with different similarity measures and hyperparameters.
- Deployment: Create an interface for users to receive recommendations.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer
from surprise import Dataset, Reader, SVD
from surprise.model_selection import train_test_split
Step 2: Load the Dataset
movies = pd.read_csv('movies.csv') #
Movie metadata
ratings = pd.read_csv('ratings.csv') #
User ratings
print(movies.head())
print(ratings.head())
Step 3: Content-Based Filtering
# Create a 'soup' of features for each movie
movies['soup'] = movies['genres'] + ' ' + movies['title']
vectorizer = CountVectorizer(stop_words='english')
soup_matrix = vectorizer.fit_transform(movies['soup'])
# Compute cosine similarity
cosine_sim = cosine_similarity(soup_matrix, soup_matrix)
Step 4: Collaborative Filtering
# Prepare data for collaborative filtering
reader = Reader(rating_scale=(0.5, 5))
data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)
trainset, testset = train_test_split(data, test_size=0.2)
# Train SVD model
model = SVD()
model.fit(trainset)
predictions = model.test(testset)
Step 5: Evaluate Collaborative Filtering
from surprise.accuracy import rmse
rmse(predictions)
5. Results and Visualization
Visualize recommendation performance using metrics.
Plot user and movie feature trends.
6. Challenges and Mitigation
Cold Start Problem: Use hybrid approaches combining content
and collaborative methods.
Data Sparsity: Apply dimensionality reduction techniques or clustering.
7. Future Enhancements
Incorporate deep learning models like Autoencoders for
advanced recommendation.
Integrate real-time user interactions for dynamic updates.
8. Conclusion
The Movie Recommendation System project highlights the
application of machine learning for personalized recommendations.
It demonstrates end-to-end development from data preprocessing to model
deployment.