Join us for the 15th incarnation of the Recommender Stammtisch hosted by plista. The event will take place on Nov 13th, starting at 7pm. Please register here.
We will have two talks:
Torben Brodt: “Latest in large scale recommendation engines and machine learning” – Torben will present some latest developments in the field of large scale recommendation engines and machine learning from the last RecSys conference.
Sebastian Schelter: “Factorbird – a Parameter Server Approach to Distributed Matrix Factorization” – We present ‘Factorbird’, a prototype of a parameter server approach for factorizing large matrices with Stochastic Gradient Descent-based algorithms. We designed Factorbird to meet the following desiderata: (a) scalability to tall and wide matrices with dozens of billions of non-zeros, (b) extensibility to different kinds of models and loss functions as long as they can be optimized using Stochastic Gradient Descent (SGD), and (c) adaptability to both batch and streaming scenarios. Factorbird uses a parameter server in order to scale to models that exceed the memory of an individual machine, and employs lock-free Hogwild!-style learning with a special partitioning scheme to drastically reduce conflicting updates. We also discuss other aspects of the design of our system such as how to efficiently grid search for hyperparameters at scale. We present experiments of Factorbird on a matrix built from a subset of Twitter’s interaction graph, consisting of more than 38 billion non-zeros and about 200 million rows and columns, which is to the best of our knowledge the largest matrix on which factorization results have been reported in the literature.