Content Based Ranking

Documents are ranked based on their contribution to the corpus using different similarity measures. It has 10 measures based on distance between document vectors, 8 based on Boolean vectors and 1 based on structure of document. For ranking documents, we are using harmonic mean as cohesion measure. We developed an efficient algorithm to calculate structured based similarities, also we modified RussellRao’s algorithm to get similarity.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites
Python3
numpy
gensim
Scikit-learn
SciPy
Matplotlib

Download and install python3 from https://www.python.org/downloads/

Install other required packages using following commands:

pip install -U scikit-learn
pip install -U scipy
pip install -U numpy
python -mpip install -U matplotlib
pip install -U gensim




Running the tests
Run the program using following command:

python ranking.py

It prints ranked document list. It also plots two heat maps showing similarity between documents and one histogram showing contribution of documents.


Authors

1. Soham Kadam
2. Ritesh Ghodrao
3. Abhishek Jain
4. Vatsal Bhanderi


Guidance

Dr. Rajendra Kumar Roul