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We implement and evaluate several algorithms in the context of developing a recommender system based on data gathered from Facebook user profiles. In particular, we look at a Matrix Factorization technique (SVD), a Clustering algorithm (K-Means), two Collaborative Filtering algorithms, a Content-Filtering approach, Latent Semantic Analysis (LSA)…

KanchanIIT/Facebook-Profile-Based-TV-Recommender-System

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Overview

This package contains a set of python scripts that allows you to scrape the Facebook Graph API for interest data of your friends list.

Usage

All you should need to do is execute "python collectData.py". It should automatically prompt you to login to Facebook and allow the application access to your Facebook data, and upon successful authentication redirect you to a success page. Results are stored in a file named friendData.xml

Files

collectData.py - Main script that collects your Facebook data, outputting it to stdout in XML form.

fbAuth.py - A script that enables you to authenticate (via OAuth) with the Facebook platform. It should automatically launch your default web browser to a page where you can choose to allow the script to access your Facebook data.

facebook.py - The Python client library for the Facebook Platform.

Model

Matrix Factorization technique (SVD)
Clustering algorithm (K-Means)
Two Collaborative Filtering algorithms
Content-Filtering approach
Latent Semantic Analysis (LSA) Link Prediction
Na¨ıve Bayes

Support

Email kanchan.besu@gmail.com with questions, concerns, or suggested improvements.

About

We implement and evaluate several algorithms in the context of developing a recommender system based on data gathered from Facebook user profiles. In particular, we look at a Matrix Factorization technique (SVD), a Clustering algorithm (K-Means), two Collaborative Filtering algorithms, a Content-Filtering approach, Latent Semantic Analysis (LSA)…

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