Under the hood: Recommendation engines in Music and Gaming-sponsored by Playtika

Y-DATA#4 meetup is focused again on recommender systems. After a great last meetup feedback and high interest in the field, this time we will talk about recommendations in music (Yandex.Music) and gaming (Playtika) industries.
Talks will be given in English.
Big Thanks to Playtika for hosting this event.

Agenda for the meetup:
18:00 - 18:30 Gathering and Mingling, Snacks & Beer
18:30 - 19:15 Feature Engineering for cold-start music recommendations (Daniil Burlakov, Yandex)
19:30 - 20:15 Matrix completion algorithms for recommendation systems (Gil Shabat, Playtika)

ABSTRACTS
********************************
"Feature Engineering for cold-start music recommendations"

Yandex Music is a top music streaming service in Russia. We build millions of personalized playlists, selecting the best tracks for our users on a daily basis with over 50 million songs in our catalog.
The quality and diversity of the recommendations are key to our user’s satisfaction. When entering a new country for the first time, achieving these qualities becomes a far greater challenge: working with new or rare content is a huge problem for the vast majority of recommendation systems which relies on historical user behavior. In this talk, we will present a set of domain-specific techniques that help overcome these hurdles and enable us to launch our service successfully in new countries.
Speaker Info: Daniil Burlakov (PhD), Yandex
Team lead of Recommender systems for Yandex Media Services (Music, Movies streaming and more). Dani graduated from Moscow state University (MSU). He holds PhD in qualitative theory of differential equations. Before Yandex Dani worked in the Laboratory of Mathematical Modeling for Dynamic Systems Simulation (MSU) where he developed training simulators based on virtual reality.

"Matrix completion algorithms for recommendation systems"

Recommendation systems play a central role in finding content or products users care about. One useful subset of algorithms used in recommendation systems, is Collaborative Filtering (CF). Unlike content based methods, when using CF, the user or the item itself does not play a role in recommendation but rather how and which users rated a particular item. In this talk, we'll discuss the Alternating-Least-Square algorithm, which is a classical method for low rank matrix completion. Along its advantages (mostly from the computational aspect), it suffers from being an NP-Hard problem, due to its non-convexity and the discrete nature of the rank. As an alternative, we'll discuss other algorithms, which are nuclear norm-based, that converge to global optimum in polynomial time with a solution that coincides with rank minimization under certain conditions.

Speaker Info: Gil Shabat (Phd), Playtika
Gil Shabat is a data science team leader in Playtika’s AI research group. Gil holds B.Sc, M.Sc and Ph.D degrees in electrical engineering, all from Tel Aviv university. Prior to joining Playtika, he was working in ThetaRay as a director of algorithm research and in a variety of other R&D positions.
His research interests include scientific computing, fast randomized algorithms and machine learning.