IS353.P12.HTCL - Social Network

Course Information

• Course: IS353.P12.HTCL - Social Network
• Lecturer: MSc. Thai Bao Tran
• Semester: 1, 2024-2025

Team Information

No.	Student ID	Full Name
1	21520596	Tran Thi Kim Anh
2	21521049	Ho Quang Lam
3	21521586	Le Thi Le Truc (Leader)
4	21521882	Le Minh Chanh

Project Information

• Project Title: MOVIE RECOMMENDATION SYSTEM SOCIAL NETWORK DESIGN
• Dataset: MovieLens32M and Movie Data from TMDB API

Overview dataset

The dataset includes the main attributes: userId and tmdbId (Movie ID), used to record user ratings for movies. The dataset is a filtered version of the original dataset, consisting of 5,345 rows and 5 attributes. These attributes will be used in the process of developing algorithms in social networks, recommending movies to users, and building a website.

Summary

• Build a user graph from a bipartite User-Movie graph
• Display and calculate basic graph metrics
• Compute centrality measures and identify key players in the graph
• Community detection
  • Explore communities using the Girvan-Newman and Louvain algorithms.
  • Recommend movies based on the community detection results.
• Link prediction
  • Predict links using Heuristics methods.
  • Recommend movies for the users at both ends of the predicted links.
• Simulate information diffusion using the Independent Cascade (IC) model
  • Generate a list of recommended movies for affected users based on the diffusion results.

Technology:

• ipynb: NetworkX, Pandas, Numpy, Scikit-learn, Matplotlib, Seaborn, Python-louvain, Community
• Website: Streamlit, FastAPI (Python)

SETUP

1. To run the SocialNetworkSource.ipynb file:

Open the terminal in the folder containing requirements.txt and run the following command to install the required libraries:

pip install -r requirements.txt

2. To run the Website:

Movie Recommendation Website

This project involves a movie recommendation website that uses community detection algorithms, link prediction, and information propagation in social networks. The website is built using Python with Streamlit and FastAPI.

Setup Instructions

To start the API and the web interface simultaneously, execute the following command in the terminal within the ./Website directory:

py run.py

Then, access the web application at http://localhost:8501/. The API will be available at http://127.0.0.1:8000/docs.