Building a Recommendation Engine using Python





Ever wondered "How did you find an old school buddy on Facebook, just like that?" , "How Spotify suggests you songs you haven't listened but are matching with your listening habits?", "How E-Commerce websites recommends you products you might like?". Well, the answer to all these question is one-Recommendation Engines. From millions of products existing on a website, it is very difficult for a user to land on a product of his need as well as his choice. But with the inception of Artificial Intelligence, a few algorithms were introduced to perform this difficult task and with the progress in technology, these algorithms were refined as well as customized as per the user requirements by various organizations. In simplest way, Recommendation Engine learns about your behavior with different techniques and recommends you products that seems suitable for you according to the algorithm.

In this talk I'll cover the following topics with code in Python:

  1. Recommendation Engines

  2. Types of recommendation Engines.

  3. Various frameworks used for the same purpose (Their comparison with python).

  4. Step wise guidance to make an engine- Data Acquisition, Data Cleaning, Implementing various models.

  5. Problems faced during developments of recommendation systems.

  6. Various libraries of Python used for the same purpose.

  7. Metrics used to measure the accuracy of a recommendation engine.

  8. Functions that can help to improve your engine for better results as the data gets bigger.


The audience must have basic knowledge of programming (not necessarily with Python). They need no prior knowledge in Machine Learning but they must have interest in this field to be able to enjoy the talk.

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Speaker Info:

Kajal Puri is currently pursuing final year of B.Tech in Computer Science at Guru Nanak Dev University, Amritsar. The academic projects undertaken by her include developing a library management system, an android app for fitness freaks and research on twitter's trending topic detection algorithm. Currently, as a part of her Summer Internship, she is developing a recommendation system for a startup named The Local Tribe.

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Section: Data Visualization and Analytics
Type: Talks
Target Audience: Beginner
Last Updated: