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. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
このコースはレコメンダシステム の専門講座に属します。
Introduction to Recommender Systems: Non-Personalized and Content-Based
提供：
習得するスキル
- Data Scientists
- Machine Learning Engineers
- Chief Technology Officers (CTOs)
- Data Engineers
- Data Analysts
シラバス - 本コースの学習内容
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.
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.
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.
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.
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.
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).
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Introduction to Recommender Systems: Non-Personalized and Content-Based からの人気レビュー
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.
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.
講師
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Michael D. Ekstrand
Assistant Professorミネソタ大学（University of Minnesota）について
レコメンダシステム専門講座について
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？
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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？
コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。
返金ポリシーについて教えてください。
学資援助はありますか？
How does this course relate to the prior versions of "Introduction to Recommender Systems"?
This specialization is a substantial extension and update of our original introductory course. It involves about 60% new and extended lectures and mostly new assignments and assessments. This course specifically has added material on stereotyped and demographic recommenders and on advanced techniques in content-based recommendation.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
