Semantic search seems like a more promising usecase than simple related articles. A big problem with classical keyword-based search is that synonyms are not reflected at all. With semantic search you can search for what you mean, not what words you expect to find on the site you are looking for.
A case related to that is "more like this" which in my mind breaks down into two forks:
(1) Sometimes your query is a short document. Say you wanted to know if there were any patents similar to something you invented. You'd give a professional patent searcher a paragraph or a few paragraphs describing the invention, you can give a "semantic search engine" the paragraph -- I helped build one that did about as well as the professional using embeddings before this was cool.
(2) Even Salton's early works on IR talked about "relevance feedback" where you'd mark some documents in your results as relevant, some as irrelevant. With bag-of-words this doesn't really work well (it can take 1000 samples for a bag-of-words classifier to "wake up") but works much better with embeddings.
The thing is that embeddings are "hunchy" and not really the right data structure to represent things like "people who are between 5 feet and 6 feet tall and have been on more than 1000 airplane flights in their life" (knowledge graph/database sorts of queries) or "the thread that links the work of Derrida and Badiou" (could be spelled out logically in some particular framework but doing that in general seems practically intractable)
Regarding item (2), Salton did talk about this and many systems from the early TREC times implemented them.
In my own work, I found that relevance feedback worked very well with only a few dozen judgements. See Chapter 7 starting on page 83 in my dissertation: https://arxiv.org/abs/1207.1847
Note also that the evaluation of Luduan in that chapter compared against state-of-the-art (for the time) back-of-words systems but also an early word embedding retrieval system (Convectis from HNC).
In my benchmarks for a service which is now running in production, hybrid search based on both keywords and embeddings performed the best. Sometimes you need exact keyword matches; other times, synonyms are more useful. Hybrid search combines both sets of results into a single, unified set. OpenSearch has built-in support for this approach.
