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Over the past 10 years, many recommendation techniques have been based onembedding users and items in latent vector spaces, where the inner product of a(user,item) pair of vectors represents the predicted affinity of the user tothe item. A wealth of literature has focused on the various modeling approachesthat result in embeddings, and has compared their quality metrics, learningcomplexity, etc. However, much less attention has been devoted to the issuessurrounding productization of an embeddings-based high throughput, low latencyrecommender system. In particular, how the system might keep up with thechanging embeddings as new models are learnt. This paper describes a referencearchitecture of a high-throughput, large scale recommendation service whichleverages a search engine as its runtime core. We describe how the search indexand the query builder adapt to changes in the embeddings, which often happen ata different cadence than index builds. We provide solutions for both id-basedand feature-based embeddings, as well as for batch indexing and incrementalindexing setups. The described system is at the core of a Web content discoveryservice that serves tens of billions recommendations per day in response tobillions of user requests.

Search-Based Serving Architecture of Embeddings-Based Recommendations