Re-ranking search results with a cross encoder model

A computer-implemented method comprising:

• receiving an intermediate recall set of products of a query;
• obtaining features of a product of the intermediate recall set of products of the query, comprising at least one of:
• (a) generating the features of the intermediate recall set of products of the query, comprising processing product information of the intermediate recall set of products of the query in parallel;
• or
• (b) receiving the features of the intermediate recall set of products of the query from a key value store;
• performing dense cross interactions between the query and the features of the product of the intermediate recall set of products using a cross-encoder model to generate a class prediction;
• generating a re-rank score for the product of the intermediate recall set of products by performing a model inference based at least on the class prediction;
• and
• re-ranking the product of the intermediate recall set of products based on the re-rank score for the product.

A non-transitory computer-readable medium storing computing instructions that, when run on a processor, cause the processor to perform operations comprising:

• receiving an intermediate recall set of products of a query;
• obtaining features of a product of the intermediate recall set of products of the query, comprising at least one of:
• (a) generating the features of the intermediate recall set of products of the query, comprising:
• segmenting product information of the intermediate recall set of products of the query into batches;
• deserializing the batches;
• vectorizing the batches, as deserialized;
• and
• processing the product information of the intermediate recall set of products of the query in parallel;
• or
• (b) receiving the features of the intermediate recall set of products of the query from a key value store;
• performing dense cross interactions between the query and the features of the product of the intermediate recall set of products using a cross-encoder model to generate a class prediction;
• generating a re-rank score for the product of the intermediate recall set of products of the query by performing a model inference based at least on the class prediction;
• and
• re-ranking the product of the intermediate recall set of products based on the re-rank score for the product.