Real-Time Search and Recommendation at Scale Using Embeddings and Hopsworks

Description

The dominant paradigm today for real-time personalized recommendations and personalized search is the retrieval and ranking architecture based on embeddings. It is a fan-out architecture where a single query produces a storm of requests on the backend. A single query will search through millions of items to retrieve hundreds of candidates that are then enriched by a feature store and ranked so only a few recommended items are presented to the user. A search should return in much less than 1 second. Retrieval and ranking architectures need significant infrastructure - an embeddings store and a feature store - to provide both the required scale and real-time performance. In this talk, we will introduce an open-source, scalable retrieval and ranking serving architecture based on open-source technology: Hopsworks Feature Store, OpenSearch, and KServe. We will describe how to build and operate personalized search and recommendation systems using a retrieval model based on a two tower embedding model, and a ranking model gradient boosted trees. We will also show how you can train your embeddings and build your embeddings store index using Hopsworks and Apache Spark. Attend this sessio…

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