Об этом курсе: 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.
Преподаватели: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and EngineeringПреподаватели: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Основные сведения | Курс 2 из 5 — Recommender Systems Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Reading: Course Structure Outline
- Видео: Configuring User-User Collaborative Filtering
- Видео: Influence Limiting and Attack Resistance; Interview with Paul Resnick
- Видео: Trust-Based Recommendation; Interview with Jen Golbeck
- Видео: Impact of Bad Ratings; Interview with Dan Cosley
- Reading: Assignment Instructions: User-User CF
- Reading: Introducing User-User CF Programming Assignment
- Видео: Programming Assignment - Programming User-User Collaborative Filtering
- Видео: Item-Item Algorithm
- Видео: Item-Item on Unary Data
- Видео: Item-Item Hybrids and Extensions
- Видео: Strengths and Weaknesses of Item-Item Collaborative Filtering
- Видео: Interview with Brad Miller
- Reading: Item-Based CF Assignment Instructions
- Reading: Introducing Item-Item CF Programming Assignment
- Видео: Programming Assignment - Programming Item-Item Collaborative Filtering
- Видео: Recommending for Groups: Interview with Anthony Jameson
- Видео: Threat Models
- Видео: Explanations
- Видео: Explanations, Part II: Interview with Nava Tintarev
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It would be better to provide other programming language such as python in honour assignment. And in the assignment should more emphasis on the algorithm not rely on too much others such as Lenskit.
(+) The course material is good with real world examples and interviews with different people.
(+) Interesting material
(-) The assignments had mistakes.
(-) There is no example provided for practice before jumping into assignments.
very nice
Diverse content that helps in understanding the basic concepts of collaborative filtering. Interviews with people specializing in different nuances of collaborative filteering were very useful.
Some thoughts on what could be improved
Pace of narration. It can be faster
More exercises are needed to get more familiar with the concepts. Each lecture should have a exercise (not just a quiz)