Intro to building a recommendation system

Emeka Boris Ama (~emeka)


2

Votes

Description:

For a website, knowing what a person needs, wants, and would want is not just a nice-to-have feature but a growing must-have. Today, recommendation systems suggest products, films/shows, music, friends/dates, etc., and it works by finding similar items to a given item or user. For that, each item first needs to be quantified and represented in a way that allows comparison against each other. In his workshop, I will begin with the origins of recommendation systems, discuss how they are built, and where they are today. also, i will review the tools and techniques used to build a recommender, and attendees will understand what type of data is necessary and will get a sense of what would make an effective recommender

Prerequisites:

Laptop

Content URLs:

github.com/emekaborisama

Speaker Info:

Hi, Emeka Boris Ama is a Data Science and Artificial intelligence Enthusiast, IBM Champion on Analytics, IBM Data Science Professional and Course Instructor at Datacamp.

He is the Co-organizer of a Pydata Port Harcourt, Open Source Africa, Speaker at Tensorflow Dev summit Port Harcourt and Instructor at School of Ai Port Harcourt.

He is passionate about building solution oriented product and educating aspiring Data Scientist and he also enjoys solving problems, troubleshooting issues, and coming up with solutions in a timely manner. He thrives in team settings, and his ability to effectively communicate with others is what drives the motion to solve a variety of problems. He's also interested in food and travel.

Speaker Links:

github.com/emekaborisama

Id: 1493
Section: Data Science, Machine Learning and AI
Type: DevSprint
Target Audience: Intermediate
Last Updated: