Recommendation Engines In Python

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Talk for PyData,Delhi 2016

K A J A L

 

Introduction to Recommendation Engines

  • Definition
  • Applications
  • Various frameworks other than python
  • Why Python is best suited for it.
  1. Better for customization

2. Parallel Computing

Types of Recommendations

  • User-User Based
  • Item-Item Based 
  • User-Item Based

Step Wise Guidance

  • Data Acquisition (from databases usually)
  • Data Cleaning (extracting out useful data)
  • Shaping the data for various algorithms

Python Libraries Used

  • pysuggest
  • NumPy
  • SciPy
  • scikit-learn(sklearn)
  • scipy.spatial
  • Crab (Python framework for building recommender engines integrated with the world of scientific Python packages (numpy, scipy, matplotlib).

Problems Faced

  • 80% of the time will be spent on data gathering and cleaning it for training purposes.
  • Differentiating which algorithm will fit to your data set in the most efficient way 

Recommendation Engines

By Kajal Puri

Recommendation Engines

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