Conjunctive filtering with embedding models

Abstract

Described herein is a technique to facilitate conjunctive filtering for an embedding-based information retrieval system. Each item in a content collection is initially analyzed to identify its property values for some set of properties. For each item, its property values are encoded in the form of a vector, and concatenated with an item embedding for the item to derive an enhanced item embedding. When a query is received, a query embedding is generated. Any filtering parameters received with or as part of the query are encoded in the form of a vector, and the vector is concatenated with the query embedding to derive an enhanced query embedding. The enhanced query embedding is then used in a 'k' approximate nearest neighbor search to identify items relevant to the query and having property values that satisfy the filtering parameters.