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Powering Recommendation Engines: Recommendation Engines

Powering Recommendation Engines: Recommendation Engines


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

As organizations become more data science aware and learn how to collect more data. Taking that data and integrating that into recommendation engines is an essential skill. In this course you will explore how recommendation engines can be created and used to provide recommendations for products and content.



Expected Duration (hours)
1.1

Lesson Objectives

Powering Recommendation Engines: Recommendation Engines

  • Course Overview
  • describe what a Recommendation Engine does, how it can be used, and the types and reasons they are used
  • compare the different types of Recommendation Engines and how they can be used to solve different recommendation problems
  • describe the process of collecting data and why data sets that can be used for learning, training, and evaluating a Recommendation Engine are needed
  • use R to import, filter, and massage data into data sets
  • describe how Similarity and Neighborhoods can be used to score users and items against another user or a new item
  • create an R function that will score a user against another user to compare their similarity
  • create an R function that will give a score to an item a user has not seen before based on other users' ratings and similarity scores
  • create an R function that finds similar users and finds products they liked which would be good to recommend to the user
  • use R to create an Item to Item similarity, or content, score to Recommend similar items
  • evaluate a Recommendation Engine by using known data and metrics to calculate the accuracy of recommendations
  • validate and score a Recommendation System using R and an evaluation data set
  • describe the types and interfaces required to build a Recommendation System
  • Course Number:
    it_dsprendj_01_enus

    Expertise Level
    Beginner