This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
提供：
レコメンダシステム専門講座ミネソタ大学（University of Minnesota）このコースについて
11,067 最近の表示
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の1コース
中級レベル
約23時間で修了
英語
字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, イタリア語, ベトナム語, ドイツ語, ロシア語, 英語, スペイン語
習得するスキル
- Summary Statistics
- Term Frequency Inverse Document Frequency (TF-IDF)
- Microsoft Excel
- Recommender Systems
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の1コース
中級レベル
約23時間で修了
英語
字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, イタリア語, ベトナム語, ドイツ語, ロシア語, 英語, スペイン語
提供：
ミネソタ大学（University of Minnesota）
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
シラバス - 本コースの学習内容
1時間で修了
Preface
This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
1時間で修了
2件のビデオ （合計41分）, 1 学習用教材
4時間で修了
Introducing Recommender Systems
This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them.
4時間で修了
9件のビデオ （合計147分）, 2 学習用教材, 2 個のテスト
10時間で修了
Non-Personalized and Stereotype-Based Recommenders
In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.
10時間で修了
7件のビデオ （合計111分）, 5 学習用教材, 9 個のテスト
3時間で修了
Content-Based Filtering -- Part I
The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.
3時間で修了
8件のビデオ （合計156分）
6時間で修了
Content-Based Filtering -- Part II
The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded.
6時間で修了
2件のビデオ （合計26分）, 3 学習用教材, 3 個のテスト
1時間で修了
Course Wrap-up
We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).
1時間で修了
2件のビデオ （合計45分）, 1 学習用教材
レビュー
INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED からの人気レビュー
BSにより2019年2月12日
One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.
DPにより2017年12月7日
Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).
IPにより2016年9月18日
it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.
NAにより2020年4月6日
The course and its content was quite interesting and easy, so I will be taking the next course in this specialization of Recommender System Specialization
レコメンダシステム専門講座について
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
よくある質問
いつ講座や課題にアクセスできるようになりますか？
この専門講座をサブスクライブすると何を行うことができるようになりますか？
学資援助はありますか？
How does this course relate to the prior versions of "Introduction to Recommender Systems"?
さらに質問がある場合は、受講者ヘルプセンターにアクセスしてください。
