Building a Recommendation Engine using Python

Kajal


37

Votes

Description:

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.

Prerequisites:

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.

Content URLs:

http://slides.com/kajalpuri/recommendation-engines#/

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.

Speaker Links:

LinkedIn:- https://in.linkedin.com/in/kajalpuri

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

Precise and informative! Piqued my curiosity further to read about Recommendation Engines. Waiting for your next write up!

tushar jadhav (~tushar3)

Looking forward for this talk

abhay puri (~abhay)

Thanks @abhay @tushar3 .

Kajal

I'd suggest that you assume that the audience is aware of the first two topics (in your proposal) and for the subsequent items, please provide a link to a dataset of reasonable size which you'd be working with and they can tinker around to reach similar results.

sankarshan mukhopadhyay (~sankarshan)

Looks interesting

Sajal Jain (~sajal2)

Looks interesting

Sajal Jain (~sajal2)

Looks interesting

Sajal Jain (~sajal2)

Quite interesting ..

Abhishek Gehlot (~abhishek28)

Looks Interesting

Nikhil Dewang (~nikhil10)

Looks Interesting

Nikhil Dewang (~nikhil10)

After learning about Machine Learning, I am all hyped up about every field which deals with data and recommendation engines sound great.

Rathod

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