À propos de ce cours : 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.
Enseigné par : Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Enseigné par : Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Informations de base | Cours 2 de 5 dans Recommender Systems Specialization |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: Course Introduction
- Reading: Course Structure Outline
- Vidéo: User-User Collaborative Filtering
- Vidéo: Configuring User-User Collaborative Filtering
- Vidéo: Influence Limiting and Attack Resistance; Interview with Paul Resnick
- Vidéo: Trust-Based Recommendation; Interview with Jen Golbeck
- Vidéo: Impact of Bad Ratings; Interview with Dan Cosley
- Vidéo: Assignment Introduction
- Reading: Assignment Instructions: User-User CF
- Reading: Introducing User-User CF Programming Assignment
- Vidéo: Programming Assignment - Programming User-User Collaborative Filtering
- Vidéo: Introduction to Item-Item Collaborative Filtering
- Vidéo: Item-Item Algorithm
- Vidéo: Item-Item on Unary Data
- Vidéo: Item-Item Hybrids and Extensions
- Vidéo: Strengths and Weaknesses of Item-Item Collaborative Filtering
- Vidéo: Interview with Brad Miller
- Vidéo: Item-Based CF Assignment Intro Video
- Reading: Item-Based CF Assignment Instructions
- Reading: Introducing Item-Item CF Programming Assignment
- Vidéo: Programming Assignment - Programming Item-Item Collaborative Filtering
- Vidéo: The Cold Start Problem
- Vidéo: Recommending for Groups: Interview with Anthony Jameson
- Vidéo: Threat Models
- Vidéo: Explanations
- Vidéo: 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.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
All in all, it is a comprehensive introduction to collaborative filtering. It allows the reader which paradigms and what tools to use in specific situations. I still have some complains with the excel assignments though.
There is an error in the assignment week 4 : the spreadsheet normalize by user instead of by item
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
The topics I am interested in covered by people who definitely has related expertise. But overall quality of the teaching materials expected to be higher. Forum is also a little bit deserted, although contains some critical hints to pass the assignments (such a hints worth to be included in the assignment descriptions itself). I want to support the course, and it is pity to give it only 4 of 5 stars, but I really expect more quality from the course I paid for.