ACM Summer School on Recommender Systems

The ACM Summer School on Recommender Systems is co-funded by SIGCHI via its conference development fund for the ACM conference series on Recommender Systems and has been granted additional support by the University of Gothenburg.
It will be held as a pre-program to this year’s RecSys conference from Monday 9th of September (afternoon) to Friday the 13th in Gothenburg (Sweden). After the summer school, participants can travel to Copenhagen for the Doctoral Symposium (Sunday Sept 15th) and/or the RecSys Conference (16-20 Sept), which is a 3 hour train ride.

Registration

Participation at the summer school requires registration via the summer school’s website. Registration will open beginning of June. Registration will remain open until the capacity limit is reached. Students accepted to doctoral symposium and from BRICS countries (that are given a stipend) get priority registration. Other students are accepted based on date of application. Early registration is likely to ensure that you can attend as it allows us to plan the accommodation. Participants from academia (academic fee) and industry (industry fee) are accepted while seats are available.

Early bird registration for PhD students (before July 31): 320 Euro
Regular fee after July 31:
- PhD students: 370 Euro
- Academics: 450 Euro
- Industry: 600 Euro

Registration include lectures and access to lecture slides/tutorials, coffee breaks and lunches.

Accommodation

Reserving an accommodation lies in the responsibility of participants. We provide pointers to recommended hotels. Take into account accommodation in Goteborg will be around 100 Euro per night.

Topics

Leaders in the field as well as promising younger researchers and people from industry volunteered to serve as speakers at this summer school. The lectures are covering a broad range of topics from an algorithmic as well as a methodological perspective and will also include hands-on sessions. In addition upcoming and trending topics such as recommending to groups or affect and personality-based recommendation approaches will be addressed.