DeepMind is widely regarded as the premier machine learning and artificial intelligence research lab. Under Alphabet, it’s collaborated with various teams at Google over the years. The latest sees DeepMind technology being leveraged for Play Store app recommendations.
Discovery is a central part of any app store, and Google’s approach features both editorial and algorithmic curation. According to DeepMind, the Play Store “supports one of the largest recommendation systems in the world” with billions of users every month.
Google takes into consideration “past user preferences” — downloads and installs — to “deliver a richer, personalised experience.” DeepMind collaborated with the Play team to “develop and improve systems that determine the relevance of an app with respect to the user.”
This, however, requires nuance – both for understanding what an app does, and its relevance to a particular user. For example, to an avid sci-fi gamer, similar game recommendations may be of interest, but if a user installs a travel app, recommending a translation app may be more relevant than five more travel apps.
At a high-level, there are three main models to the recommendation system: a candidate generator, a reranker, and a model to optimize for multiple objectives. A blog post today goes in-depth on each part.
The candidate generator is a deep retrieval model that can analyse more than a million apps and retrieve the most suitable ones. For each app, a reranker, i.e. a user preference model, predicts the user’s preferences along multiple dimensions. Next these predictions are the input to a multi-objective optimisation model whose solution gives the most suitable candidates to the user.
The DeepMind for Google team also shed some light into the collaboration process, noting daily communication:
Because the Play Store and DeepMind teams worked so closely together and communicated on a daily basis, we were able to take product requirements and constraints into consideration throughout the algorithm design, implementation, and final testing phases, resulting in a more successful product.
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