关于此课程： This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance. Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration. Both groups will produce a capstone report documenting the analysis, the selected solution, and the justification for that solution.
教学方： Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University教学方： Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
| 基本信息 | 课程 5（共 5 门，Recommender Systems Specialization ） |
| 承诺学习时间 | 1-3 weeks of study, 3-5 hours per week |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- Reading: Capstone Assignment (all versions combined)
- 视频: Capstone Wrap-Up
- Reading: Thank you!
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The problem statement is well structured, and emulates a real-life situation very well. From experience, I can assert that recommendation problems are pitched in a very similar manner as the one mentioned here. The guidelines assist in the student's framing of the solution. One week however, might not be sufficient for completion from planning to execution to summarizing, especially if he/she is interested in the Honors version. In all, this is a must take for anyone to gain practical experience in this field, however, should start at it at least two weeks prior to the starting date, so that he/she doesn't get hurried to complete it.
KS
This was a very good course, or rather, an end to a fine specialization. When I started out, it felt like the job would be very easy since all kind of data were available. But it proved to be anything but. There was a lot of experimentation and mixing-matching involved. But again, I believe this project has got more to do with doing good planning and choosing evaluation methods to meet business needs, rather than on implementation.