📌 Session #1 focuses on following topics:
● Discussion on participants' goals for the study group
● Overview: what are the different types of recommender systems in common use, and what are they used for
● Collecting user data, producing non-personalized recommendations, and handling the cold-start problem
● Collaborative filtering: Theory, examples and how to evaluate a recommender system
Going forward, we would like to expand focused discussion to directions aligned with our members' interests. The study sessions are organized by Aki Saarinen http://linkedin.com/in/akisaarinen
📌For each study session we will have:
● A set of home study materials we hope participants take a look at before joining. We may base some of the discussion on books, but it's optional and we also link to free online resources.
● A short summary presentation of the topics for the day at the beginning of the session (prepared by organizer). This will hopely get us all the a similar starting line.
● A set of discussion areas where we hope participants proactively bring in their questions, experiences, and thoughts, so we can talk together in an interactive format.
This is not a lecture series, but rather a forum for us all to learn more on these super interesting timely topics.
You can join regardless of your current level, as long as you are interested in understanding how these systems work under-the-hood.
● Book: Practical Recommender Systems by Kim Falk
* Book info: https://www.manning.com/books/practical-recommender-systems
* Example project discussed in the book: https://github.com/practical-recommender-systems/moviegeek
● Introductory free online articles:
● Stanford Course: Mining Massive Datasets (Lecture videos available on Youtube):
* Lecture 41 - Overview of recommender systems: https://youtu.be/1JRrCEgiyHM
* Lecture 42 - Content-based Recommendations: https://youtu.be/2uxXPzm-7FY
* Lecture 43 - Collaborative Filtering: https://youtu.be/h9gpufJFF-0
* Lecture 44 - Implementing Collaborative Filtering: https://youtu.be/6BTLobS7AU8
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