關於此課程：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.
此課程適用人群：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.
教學方： Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering教學方： Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| 基本信息 | 課程 1（共 5 門，Recommender Systems Specialization ） |
| 級別 | 中級 |
| 承諾學習時間 | 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. |
| 語言 | English |
| 硬件要求 | For honors track must be able to run substantial computations using Java (e.g., 4GB or more ram). |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
- 視頻: Intro to Course and Specialization
- 阅读: Notes on Course Design and Relationship to Prior Courses
- 視頻: Preferences and Ratings
- 視頻: Predictions and Recommendations
- 視頻: Taxonomy of Recommenders I
- 視頻: Taxonomy of Recommenders II
- 視頻: Tour of Amazon.com
- 視頻: Recommender Systems: Past, Present and Future
- 阅读: About the Honors Track
- 視頻: Introducing the Honors Track
- 視頻: Honors: Setting up the development environment
- 阅读: Downloads and Resources
- 視頻: Summary Statistics I
- 視頻: Summary Statistics II
- 阅读: External Readings on Ranking and Scoring
- 視頻: Demographics and Related Approaches
- 視頻: Product Association Recommenders
- 阅读: Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders
- 視頻: Assignment #1 Intro Video
- 阅读: Assignment Intro: Programming Non-Personalized Recommenders
- 視頻: Assignment Intro: Programming Non-Personalized Recommenders
- 阅读: LensKit Resources
- 阅读: Rating Data Information
- 視頻: TFIDF and Content Filtering
- 視頻: Content-Based Filtering: Deeper Dive
- 視頻: Entree Style Recommenders -- Robin Burke Interview
- 視頻: Case-Based Reasoning -- Interview with Barry Smyth
- 視頻: Dialog-Based Recommenders -- Interview with Pearl Pu
- 視頻: Search, Recommendation, and Target Audiences -- Interview with Sole Pera
- 視頻: Beyond TFIDF -- Interview with Pasquale Lops
- 阅读: Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)
- 阅读: Tools for Content-Based Filtering
- 阅读: CBF Programming Intro
- 視頻: Honors: Intro to programming assignment
- 阅读: Related Readings
- 視頻: Psychology of Preference & Rating -- Interview with Martijn Willemsen
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
I think I am on the right track to changing my career from java engineer from data scientist, this course is one of the best start point
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.
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.
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I would like to have more detail and help for honors track especially for people like me who do not have much programming experience and want to learn how to implement recommender system. I am unable to solve the assignment and i still need some help. Would be great if the solutions of the honors track should be available to those who want to learn and not just for the sake of getting certificate