Practical Recommendations for Building Recommender Systems


In this talk we’re going to walk through a design of the typical recommender system operating that is operating at a large scale. As part of this talk we’re going to do a few deep-dives in the various aspects of the recommender systems and will cover various topics that are critical for the recommender system design, but usually are not receiving enough attention, such as:

Desired properties for multi-stage ranking system
Sparse features for ranking models
Limitations of the current commonly models used approaches and possible ways to address those.


Andrey Malevich is a technical lead manager leading a team working Graph Learning Systems at Facebook AI. We developing cutting edge technologies operating at a Facebook scale, that are focusing on recommendation systems.

Prior to this Andrey was a technical lead manager working at Personalization Platform, that is powering largest AI-workloads at Facebook.

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