关于此课程： 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.
此课程适用人群： This course is appropriate for learners who have a basic understanding of statistics. It can be useful both for those exploring applied machine learning and data mining, and for those focused on technology-supported marketing and commerce.
制作方： University of Minnesota
教学方： Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
教学方： Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| 基本信息 | 课程 1（共 5 门，Recommender Systems Specialization ） |
| 级别 | 中级 |
| 承诺学习时间 | 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. |
| 语言 | English |
| 硬件要求 | For honors track must be able to run substantial computations using Java (e.g., 4GB or more ram). |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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.
2 个视频, 1 个阅读材料
- 视频: Intro to Course and Specialization
- Reading: Notes on Course Design and Relationship to Prior Courses
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.
9 个视频, 2 个阅读材料
- 视频: Preferences and Ratings
- 视频: Predictions and Recommendations
- 视频: Taxonomy of Recommenders I
- 视频: Taxonomy of Recommenders II
- 视频: Tour of Amazon.com
- 视频: Recommender Systems: Past, Present and Future
- Reading: About the Honors Track
- 视频: Introducing the Honors Track
- 视频: Honors: Setting up the development environment
- Reading: Downloads and Resources
已评分: Closing Quiz: Introducing Recommender Systems
已评分: Honors Track Pre-Quiz
第 2 周
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.
7 个视频, 5 个阅读材料
- 视频: Summary Statistics I
- 视频: Summary Statistics II
- Reading: External Readings on Ranking and Scoring
- 视频: Demographics and Related Approaches
- 视频: Product Association Recommenders
- Reading: Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders
- 视频: Assignment #1 Intro Video
- Reading: Assignment Intro: Programming Non-Personalized Recommenders
- 视频: Assignment Intro: Programming Non-Personalized Recommenders
- Reading: LensKit Resources
- Reading: Rating Data Information
已评分: Assignment #1: Response #1: Top Movies by Mean Rating
已评分: Assignment #1: Response #2: Top Movies by Count
已评分: Assignment #1: Response #3: Top Movies by Percent Liking
已评分: Assignment #1: Response #4: Association with Toy Story
已评分: Assignment #1: Response #5: Correlation with Toy Story
已评分: Assignment #1: Response #6: Male-Female Differences in Average Rating
已评分: Assignment #1: Response #7: Male-Female differences in Liking
已评分: Non-Personalized Recommenders
已评分: Programmming Non-Personalized Recommenders
第 3 周
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.
8 个视频
- 视频: TFIDF and Content Filtering
- 视频: Content-Based Filtering: Deeper Dive
- 视频: Entree Style Recommenders -- Robin Burke Interview
- 视频: Case-Based Reasoning -- Interview with Barry Smyth
- 视频: Dialog-Based Recommenders -- Interview with Pearl Pu
- 视频: Search, Recommendation, and Target Audiences -- Interview with Sole Pera
- 视频: Beyond TFIDF -- Interview with Pasquale Lops
第 4 周
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.
2 个视频, 3 个阅读材料
- Reading: Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)
- Reading: Tools for Content-Based Filtering
- Reading: CBF Programming Intro
- 视频: Honors: Intro to programming assignment
已评分: Assignment #2 Answer Form
已评分: Content-Based Filtering
已评分: CBF Programming Assignment
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).
2 个视频, 1 个阅读材料
- Reading: Related Readings
- 视频: Psychology of Preference & Rating -- Interview with Martijn Willemsen
常见问题解答
运作方式
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Certificates
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制作方
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.
价格
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评分和审阅
I am very new to recommendation system and yet able to comprehend the lessons. The best thing is explaining the system with example. Walking through Amazon.com and explaining content based and collaborative filtering is easy to grasp.
I think some of the interviews didn't really give me great insights. I know this is only an introduction, but I was expecting more fields than movies. I am overly critical though, all in all a very good way to understand recommendation systems.
It would be better to make practice on Python.
TB
Fantastic course. Lecturers have extensive experience in this field. Lectures include interviews with people who have successfully implemented recommender systems in their products or who are researching the permutations, challenges and extensions to recommender system development. Not only does the course provide the chance to build your own recommender systems (optional) but also highlights the complexities and opportunities for refining and improving recommendations. I highly recommend this course to anyone building recommendation systems.