Skip to content

Latest commit

 

History

History
53 lines (42 loc) · 2.34 KB

README.md

File metadata and controls

53 lines (42 loc) · 2.34 KB
Raw

Recommender System

CS F469 IR Assignment - 3

Problem Statement:

The task is to compare various techniques used in implementing Recommender Systems on the basis of their errors using Root Mean Square Error, Precision on top K and Spearman Rank Correlation. Also compare their overall running time and prediction time. The techniques implemented and compared are:

  1. Collaborative Filtering.
  2. Collaborative Filtering using Baseline approach.
  3. Singular Value Decomposition(SVD).
  4. SVD with 90% energy.
  5. CUR.
  6. CUR with 90% energy.

About the project

Dataset used - Movie lens Dataset is used in this assignment consisting of 6040 users rating of 3883 movies

Have a look at the file Design Document. It includes the concepts used along with the time taken for each implementation step.

Project By:

How to run the code

  1. Clone the repository : https://github.com/JuiP/RecommenderSystem.git

  2. cd RecommenderSystem

  3. Change the path to ratings.dat in the python code, wherever you have saved the ml-1m folder(preferably in the same folder) and run the python script for each method.

  4. Run file:

           python3 collab_user.py
       python3 collab_global.py
       python3 cur.py
       python3 cur_90.py
       python3 svd.py
       python3 svd_90.py

  5. The Root Mean Square Error(RMSE), Precision on top K, Spearman Rank Correlation and Time taken for prediction is printed at the end of each file execution.:)

Dependencies/modules used

  • time
  • math
  • pandas
  • Numpy
  • random