• Recommendation Systems (study sessions)

    Tokyo Metropolitan Central Library

    📌 Session #2 focuses on following topics: ● Aki Saarinen: Learnings from building a simple collaborative filtering recommender on the MovieLens dataset ● Aakash Nand: Topic TBD ● We are still open for a third topic, please let us know if you’re interested: https://forms.gle/PHSinqG5f8nho3wk7 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 couple short presentations on topics to spark a discussion. If you’d like to present, please contact the organizers on MLT Slack (#recommendation_systems), or join the meetup and voice your interest. It can be for example learnings from a recommender related project you’re working on, an interesting paper you read, or any other relevant topic you’d like to share with our community. ● Discussion 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. 📌 RESOURCES ● Book: Practical Recommender Systems by Kim Falk * A good introduction to recommender systems * 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: * https://www.coursera.org/learn/recommender-systems-introduction * https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada * https://hackernoon.com/introduction-to-recommender-system-part-1-collaborative-filtering-singular-value-decomposition-44c9659c5e75 ● 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 💙 –– MLT PATRON –– Always on the waiting list for MLT events? Become a MLT Patron and get early access to workshops, study groups and talks. https://www.patreon.com/MLTOKYO –– FIND RESOURCES –– Github: https://github.com/Machine-Learning-Tokyo Youtube: https://www.youtube.com/MLTOKYO Slack: https://goo.gl/WnbYUP –– RECRUITING –– MLT events are for community building and knowledge sharing. We politely ask that company representatives, recruiters and consultants looking to hire or sell their services do not come to this event. –– CODE OF CONDUCT –– MLT promotes an inclusive environment that values integrity, openness and respect. https://github.com/Machine-Learning-Tokyo/MLT_starterkit