Welcome to RS_c, the central platform for the RecSys community. We provide curated lists of recommender-systems datasets, algorithms, books, conferences and many resources more. Maybe most importantly, we publish the latest recommender-system news. If you want your news to be reported on RS_c, read here.
LinkedIn introduces “GDMix”, a deep ranking personalization framework
October 2, 2020
Jun Shi from LinkedIn announced the release of GDMix (GitHub), a deep ranking personalization framework. GDMix stands for Generalized Deep Mixed Model and is…
a solution created at LinkedIn to train these kinds of [ranking] models efficiently. It breaks down a large model into a global model (a.k.a. “fixed effect”) and a large number of small models (a.k.a. “random effects”), then solves them individually. This divide-and-conquer approach allows for efficient training of large personalization models with commodity hardware. An improvement from its predecessor, Photon-ML, GDMix expands supports deep learning models. It can be applied to a variety of search and recommendation tasks
LinkedIn seems to use that library for all kind of recommendations and search and states the library can be used for job recommendations, ads ranking, content search, movie recommendations, app store recommendations and more.