About this course: 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.
Taught by: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Taught by: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Basic Info | Course 2 of 5 in the Recommender Systems Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Course Introduction
- Reading: Course Structure Outline
- Video: User-User Collaborative Filtering
- Video: Configuring User-User Collaborative Filtering
- Video: Influence Limiting and Attack Resistance; Interview with Paul Resnick
- Video: Trust-Based Recommendation; Interview with Jen Golbeck
- Video: Impact of Bad Ratings; Interview with Dan Cosley
- Video: Assignment Introduction
- Reading: Assignment Instructions: User-User CF
- Reading: Introducing User-User CF Programming Assignment
- Video: Programming Assignment - Programming User-User Collaborative Filtering
- Video: Introduction to Item-Item Collaborative Filtering
- Video: Item-Item Algorithm
- Video: Item-Item on Unary Data
- Video: Item-Item Hybrids and Extensions
- Video: Strengths and Weaknesses of Item-Item Collaborative Filtering
- Video: Interview with Brad Miller
- Video: Item-Based CF Assignment Intro Video
- Reading: Item-Based CF Assignment Instructions
- Reading: Introducing Item-Item CF Programming Assignment
- Video: Programming Assignment - Programming Item-Item Collaborative Filtering
- Video: The Cold Start Problem
- Video: Recommending for Groups: Interview with Anthony Jameson
- Video: Threat Models
- Video: Explanations
- Video: Explanations, Part II: Interview with Nava Tintarev
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
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| Purchase Course | Audit | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Available | Not available |
| Receive a final grade | Available | Not available |
| Earn a shareable Course Certificate | Available | Not available |
Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!
Loved it...many thanks Prof. Joe for bringing this content to Coursera
a great class, I learned some insight in these algorithms
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Great course, nice theory and interesting exercise with the sheets and making actual Java programs to implement the algorithms. I would love to see some more in-depth probability theory, and considerations about when the algorithms deviate from the theory, or connections to other theories, but I suppose the course is more accessible and interesting like this. The interviews are probably my favorite part!