Understanding Algorithms for Recommendation Systems

Recommendations help monetize user behavior data that businesses capture. This course is all about identifying user-product relationships from data using different recommendation algorithms.
Course info
Rating
(59)
Level
Beginner
Updated
Feb 1, 2017
Duration
2h 13m
Table of contents
Description
Course info
Rating
(59)
Level
Beginner
Updated
Feb 1, 2017
Duration
2h 13m
Description

In addition to monetizing user behavior data, recommendation algorithms also help extract actionable recommendations from raw user ratings/purchases data. This course, Understanding Algorithms for Recommendation Systems, will cover the different types of Recommendation algorithms - Content-Based Filtering, Collaborative Filtering, and Association Rules Learning and when to use each of these types. You'll also learn about the specific algorithms such as the Nearest Neighbors model, Latent Factor Analysis and the Apriori Algorithm and implement them on real data sets. Finally, you'll learn about mining for rules that relate different products. By the end of this course, you'll be able to choose the recommendation algorithm that fits your problem and dataset, and apply it to find relevant recommendations.

About the author
About the author

Swetha loves playing with data and crunching numbers to get cool insights. She is an alumnus of top schools like IIT Madras and IIM Ahmedabad.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone! My name is Swetha Kolalapudi, and welcome to my course, Understanding Algorithms for Recommendation Systems. I am the co-founder of a startup called Loonycorn. Recommendations are a great way to monetize user behavior data that businesses capture. Recommendation algorithms identify hidden relationships among users and products using draw user ratings data. Understanding those relationships leads to targeted relevant recommendations for your users. This course will cover the different types of recommendation algorithms, content-based filtering, collaborative filtering, and association rules learning, and when to use each type of algorithm. By the time you are done, you'll be able to implement complex algorithms in Python to identify recommendations from datasets like book ratings by users or grocery purchases. Some of the major topics that we will cover include finding users similar to a given user, identifying hidden factors that influence user behavior, and mining for rules that relate to different products. By the end of this course, you'll be able to choose the recommendation algorithm that fits your problem and dataset and apply it to find relevant recommendations. Before beginning this course, you should be familiar with Python at the very basic level. I hope you'll join me on this journey to understand algorithms for recommendation systems at Pluralsight.