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Building Recommenda tion Engines

Get to know more about how Netflix, YouTube, Amazon and so many of your favourite services find out the next best thing for you!

Abstract

A recommendation engine filters the data using different algorithms and recommends the most relevant items to users. It first captures the past behavior of a customer and based on that, recommends products which the users might be likely to buy. We will cover various types of recommendation engine algorithms and fundamentals of creating them in Python. We will also see the mathematics behind the workings of these algorithms. Finally, we will create our own recommendation engine using matrix factorization.

Outline

  • 1.What are recommendation engines?
  • 2.How does a recommendation engine work?
    1. Content based filtering
    2. Collaborative filtering
  • 3. Building collaborative filtering model from scratch.
  • 4.The cold start problem.
  • 5.Introduction to matrix factorization.

Requirements

Participants must have or try to have the following few requirements

  • 1.Intermediate programming skills in Python.
  • 2. Basic knowledge of Machine learning concepts.

Speaker bio

workshop

Shahul ES is a third year undergraduate student at Govt Model Engineering College. He is a Kaggle 2x Expert and is one of the top 100 datascientist in Kernel section of Kaggle. Shahul is currently working as an intern in Concepts.ai and is an organiser of School of AI, Kochi.

Links

MEC.conf Team

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Akhil Seshan
IEEE CS MEC SBC Chairman

+91 7558047349

P Gautham Dileep
IETE SF MEC Chairman

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Kurian Benoy
FOSSMEC Chairman

+91 9400125402

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Devdutt Shenoi
IEEE CS MEC SBC Secretary

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