Monthly Subscription Starting at AED 99 + VAT

Nearest Neighbor Collaborative Filtering
  • Level Foundation
  • Duration 15 hours
  • Course by University of Minnesota
  • Offered by

About

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.

Modules

Course Introduction

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Course Introduction

1 Readings

  • Course Structure Outline

User - User Collaborative Filtering

    • 5 Videos
Show info about module content

5 Videos

  • User-User Collaborative Filtering
  • Configuring User-User Collaborative Filtering
  • Influence Limiting and Attack Resistance; Interview with Paul Resnick
  • Trust-Based Recommendation; Interview with Jen Golbeck
  • Impact of Bad Ratings; Interview with Dan Cosley

User-User CF Assignment and Quiz

    • 1 Videos
    • 1 Readings
    • 2 Assignment
Show info about module content

2 Assignment

  • User-User CF Answer Sheet
  • User-User Collaborative Filtering Quiz

1 Videos

  • Assignment Introduction

1 Readings

  • Assignment Instructions: User-User CF

Programming User-User Collaborative Filtering

    • 1 Videos
    • 1 Readings
    • 1 Programming
Show info about module content

1 Programming

  • User-User CF Programming Assignment

1 Videos

  • Programming Assignment - Programming User-User Collaborative Filtering

1 Readings

  • Introducing User-User CF Programming Assignment

Introduction to Item-Item Collaborative Filtering

    • 6 Videos
Show info about module content

6 Videos

  • Introduction to Item-Item Collaborative Filtering
  • Item-Item Algorithm
  • Item-Item on Unary Data
  • Item-Item Hybrids and Extensions
  • Strengths and Weaknesses of Item-Item Collaborative Filtering
  • Interview with Brad Miller

Assignment Intro Video

    • 1 Videos
    • 1 Readings
    • 4 Assignment
Show info about module content

4 Assignment

  • Item Based Assignment Part l
  • Item Based Assignment Part II
  • Item Based Assignment Part III
  • Item Based Assignment Part IV

1 Videos

  • Item-Based CF Assignment Intro Video

1 Readings

  • Item-Based CF Assignment Instructions

Programming Item-Item Collaborative Filters

    • 1 Videos
    • 1 Readings
    • 1 Programming
Show info about module content

1 Programming

  • Item-Item CF Programming Assignment

1 Videos

  • Programming Assignment - Programming Item-Item Collaborative Filtering

1 Readings

  • Introducing Item-Item CF Programming Assignment

Advanced Collaborative Filtering Topics

    • 5 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Item-Based and Advanced Collaborative Filtering Topics Quiz

5 Videos

  • The Cold Start Problem
  • Recommending for Groups: Interview with Anthony Jameson
  • Threat Models
  • Explanations
  • Explanations, Part II: Interview with Nava Tintarev

Auto Summary

Discover the art of personalized recommendation systems with the "Nearest Neighbor Collaborative Filtering" course. This foundational course, expertly crafted for the Data Science and AI domain, is designed to equip learners with the essential techniques for creating tailored suggestions through nearest-neighbor algorithms. Under the guidance of experienced instructors from Coursera, you will delve into user-user collaborative filtering, a method that matches users with similar tastes to recommend items. You'll also explore the popular item-item collaborative filtering, which leverages global product associations to make personalized recommendations based on individual user ratings. Throughout the course, you'll implement various algorithmic approaches, uncovering their advantages and limitations. Spanning 900 minutes of immersive content, the course caters to beginners and offers flexible subscription options, including Starter and Professional plans. Ideal for aspiring data scientists and AI enthusiasts, this course provides the foundational knowledge needed to excel in the world of recommendation systems. Join now and start your journey toward mastering personalized recommendations!

Joseph A Konstan
Instructors

Joseph A Konstan

Michael D. Ekstrand
Instructors

Michael D. Ekstrand