Phase-1 Submission Template
Student Name: R.Harini
Register Number:510623104027
Institution: C. Abdul Hakeem College of Engineering and Technology
Department: Computer Science and Engineering
Date of Submission: 26.04.2025
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1. Problem Statement
In the era of overwhelming entertainment choices, users often struggle to find movies that align with their
unique tastes and preferences. Traditional recommendation systems rely on basic filtering techniques,
which may not fully capture the complexity of human interests and emotional connections. This project
aims to enhance the movie-watching experience by leveraging AI matchmaking algorithms to deliver
highly personalized movie recommendations based on user preferences, behavior, and contextual factors.
The goal is to create a system that feels like it truly "understands" what each user wants to watch.
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2. Objectives of the Project
To build an AI-based recommendation engine tailored to individual movie preferences.
To use user profiles, ratings, and viewing history to predict relevant movie suggestions.
To explore collaborative and content-based filtering, and advanced AI matchmaking approaches.
To evaluate the system for precision, recall, and user satisfaction.
To simulate a personalized movie recommendation experience in a prototype.
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3. Scope of the Project
User data inputs: genre preferences, past ratings, watch history, emotional context.
Models used: collaborative filtering, content-based filtering, hybrid systems with neural networks.
Limited to offline simulations using sample datasets (no live integration with streaming platforms).
Personalization limited to user data available in the dataset or synthetic profiles.
Final outcome will be an interactive prototype or dashboard showcasing recommendations.
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4. Data Sources
Dataset: MovieLens 100K Dataset
Source: GroupLens (https://grouplens.org/datasets/movielens/)
Type: Public and static
Features: User IDs, movie titles, genres, user ratings, timestamps.
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5. High-Level Methodology
Data Collection: Download MovieLens dataset from GroupLens.
Data Cleaning: Remove missing values, ensure consistent formats, normalize rating scales.
Exploratory Data Analysis (EDA): Analyze user behavior patterns, popular genres, and rating trends.
Feature Engineering: Create user-movie interaction matrices, derive features such as average rating, genre
affinity.
Model Building: Implement:
Content-based filtering
Collaborative filtering (user-user and item-item)
Matrix Factorization (e.g., SVD)
Neural networks for AI matchmaking (e.g., deep learning embeddings)
Model Evaluation: Use metrics like RMSE, MAE, precision@k, recall@k to validate recommendations.
Visualization & Interpretation: Display personalized recommendation lists, similarity scores, and feature
heatmaps.
Deployment (Optional): Prototype an interactive recommendation UI using Streamlit or Gradio.
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6. Tools and Technologies
Programming Language: Python
Notebook/IDE: Jupyter Notebook / Google Colab
Libraries:
Data Handling: pandas, numpy
Visualization: seaborn, matplotlib, plotly
Modeling: scikit-learn, surprise, TensorFlow/Keras
Recommendation: LightFM, implicit, scikit-surprise
Optional Tools for Deployment: Streamlit, Gradio
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7. Team Members and Roles
R. Harini – Team Lead & Model Building
Oversees project development, implements recommendation algorithms and deep learning models.
H. Ayisha Siddiqha – Data Collection & Preprocessing
Collects and prepares data, handles data cleaning, and builds user preference profiles.
P. Hemapriya – Exploratory Data Analysis & Visualization
Performs trend analysis and creates graphs to interpret viewing patterns and genre distributions.
K. Magizharasi – Evaluation & Report Writing
Evaluates model performance, validates recommendation quality, and compiles the project report and
presentation.
