gravity-at-recsys2016

RecSys 2016 – Part II. – Gravity @ RecSys 2016

by Balázs Hidasi

 

This is the second of three posts on the RecSys 2016 conference. Last week I wrote about the conference in general. This week I’ll discuss what Gravity contributed to the conference.

Since the conference was overseas, the Gravity contingent at RecSys was small this year, compared to last year. Only our CEO (Domonkos Tikk) and myself as the Head of Research represented Gravity. However, the contributions of Gravity were plentiful. We organized a workshop, had a long and two PPF papers, as well as a poster.

DLRS 2016

We co-organized the 1st Deep Learning for Recommender Systems (DLRS) workshop with Alexandros Karatzoglou from Telefonica Research and researchers from Israel (IBM Research & Ben Gurion University). The idea of organizing the workshop came to us naturally, since we were early adopters of deep learning in the RecSys community: we launched joint research collaboration with Alex last summer in this topic and have been working in this area ever since. We wanted to spread the word about deep learning in this community and encourage research, so we proposed to have this workshop. We were joined by another group who had a similar idea. The contribution of co-organizers showed significant variance, but this is pretty much what you can expect with seven organizers. 🙂

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recsys2016_boston

RecSys 2016 – Part I. – The conference from a research perspective

by Balázs Hidasi

 

The 10th conference in the ACM RecSys conference series was held in Boston between September 15 and 19. Upon returning from the conference (and a few days of vacation dedicated to discovering Boston), I decided to post about my experiences.

This is the first in a series of three blog posts on RecSys 2016. In this post, I write about the conference from a research perspective and discuss the popular research directions of the conference and the field in general. The next post will discuss Gravity’s contribution, which includes the organization of the Deep Learning for Recommender Systems workshop, presenting a long paper, and more. The final post will conclude this series with my best paper picks from RecSys 2016. Check back next week for the second blog post!

General thoughts

Last year I was kind of disappointed with the technical quality of RecSys and hoped that it was due to everyone working on new exciting research directions and wanted to roll the old stuff out before moving on. It felt like the calm before the storm and you could feel that the community had been already working on novel research projects, but only a few was ready for the public. The only question was whether these exciting topics will be discussed this year?

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