In many search scenarios, the user does not start from an empty query box, but from an existing result.
A user opens an article and wants to find related material. A buyer views a product card and looks for close alternatives. A support engineer investigates an incident and wants to see earlier cases with the same symptoms. In all these situations, the user already has a relevant document to start from.
This scenario is traditionally called More Like This (MLT): a function for finding documents similar to the selected one. In this article, MLT means search that starts from a known document, not from a newly typed query.
The classic MLT approach, or similar-document search, was based on comparing textual matches. Modern implementations increasingly use embeddings: numerical representations of documents. A search index stores embeddings as vectors, and the search system can find documents with close vector representations.
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