Welcome to the second quarter of UC San Diego's Data Science Capstone in Recommender Systems!
Officially this is "DSC-180 B Section A05" for Winter, 2021.
Please review the syllabus for the last quarter (below) for guidelines about health, integrity and inclusion.
- Mentor (Colin Jemmott) email: cjemmott@ucsd.edu
- Zoom
- Slack
Welcome to UC San Diego's Data Science Capstone in Recommender Systems! Officially this is "DSC-180 A Section A06" for Fall, 2020.
This quarter we will replicate a recent paper about, well, replication failures.
The quarter will end with a proposal for your capstone project. My only real rule about this is that it must be a recommender system that actually works.
"Actually works", in this context, means that you are able to validate that it provides humans real value. This probably means it lives on the internet, and does something useful or brings people joy.
- DSC180 Main Website
- DSC180 Main Schedule
- Mentor (Colin Jemmott) email: cjemmott@ucsd.edu
- Zoom
- Slack
First, your top priority right now should be making sure you stay safe and healthy. Your second priority should be making sure your family, friends, and community are safe and healthy. I hope that together we can learn some data science too, but never at the cost of health or safety.
Second, this class will be entirely remote - no in-person anything. It is going to be a bit rough, and we will all make mistakes. I encourage you to be generous and forgiving, but also to give clear and frequent feedback. I will try to do the same.
Third, I want to recognize that this is going to be hard for you. There are students in this class that are all over the world, that don't have access to good computers, reliable internet, and who are suddenly working in less-than-optimal conditions. There is also the stress of living during a global pandemic, social unrest and a major election! Personally, I know I am not doing my best work right now because I am distracted and worried. Please reach out if there is anything I can do to help you in this class. I want you all to succeed.
For this class, the key to academic integrity is accurately representing the status and authorship of your work. I strongly encourage you to read the official UCSD policy on integrity of scholarship.
I am committed to an inclusive learning environment that respects our diversity of perspectives, experiences and identities. You, as a student in this course, are also responsible for maintaining an environment where your fellow students feel safe and respected.
In my opinion, the key to this is recognizing the inherent worth and dignity of every person. If there is a way you could feel more included please let me know via email.
If you need to brush up on recommender systems, you might find these links helpful:
- UCSD's CSE158: "Web Mining and Recommender Systems" with McAuley. If you haven't taken this class, you should take it this quarter. Notes and such from previous versions are online.
- Google's "Recommendation Systems" course is free, and takes about four hours to complete.
- The book "Practical Recommender Systems" by Falk is more approachable, and does a good job discussing the pitfalls that happen in real-world applications. I don't love the informal tone, and the code samples aren't my favorite, but the advice is solid.
- "Recommender Systems: The Textbook" by Aggarwal is a nice, fairly complete and technical reference. I often pick it up when I am stuck.
Here are the papers referenced in the main paper we are reading:
- CMN: https://arxiv.org/pdf/1804.10862.pdf
- MCRec: https://dl.acm.org/doi/pdf/10.1145/3219819.3219965
- CVAE: http://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/p305-li.pdf
- CDL: http://wanghao.in/paper/KDD15_CDL.pdf
- NCF: https://arxiv.org/pdf/1708.05031.pdf
- SpectralCF: https://www.cs.uic.edu/~clu/doc/recsys18_spectralCF.pdf
- Mult-VAE: https://arxiv.org/pdf/1802.05814.pdf
- More about Colin, which includes a link to a fun podcast I did with UCSD's DS3.