Recommender System

CPSC 8650 - Data Mining






Team 11
Bhargav Golla
Chris Gropp

What is Recommender System?




  • Making recommendations based on previous activity
  • Amazon Product recommendations
  • Netflix Challenge
  • Collaborative Filtering technique: Search large group of users
    and filter small subset with tastes similar to a user

What are we doing?





  • Build a recommender system using standard libraries available
  • Build a recommender system using similarity functions and compare with the standard recommender built previously

WHAT ARE WE GAINING?





A clear understanding of working of a recommender system

Dataset




GroupLens' Movie Lens Datasets of sizes 100K and 1M
Chosen because it has the required attributes (user ratings) and is widely used by many research teams and hence is preprocessed

Tools Used


  • PredictionIO which is built on Apache Mahout, MongoDB and Hadoop
  • Matlab

Results with standard recommender


With 100K dataset,
Asynchronous loading - Around 6.65 minutes
Synchronous loading - Around 7 minutes

With 1M dataset,
Asynchronous loading - Around 53.5 minutes
Synchronous loading - Around 57.5 minutes

Results with Matlab recommender




With 100K Dataset, time taken for recommendations: 
8 minutes

What do we have?


A Python CLI script which will ask for user to enter user ID he needs recommendations for and we show top 10 movie recommendations for that user.

A MATLAB script to generate top 10 recommendations for a user using either Euclidean Distance Similarity or Pearson Correlation Coefficient similarity function

Future Work


In Matlab Recommender:
Remove user rated items from recommendations

Test Matlab recommender on 1M dataset

In general:
Compare two recommenders on recommendations

Make our code transpose-able to allow item-based recommendation



Thank you




Questions?

Recommender System

By bhargavgolla

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Recommender System

Presentation of term project developed by Chris Gropp and Bhargav Golla at School of Computing, Clemson University

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