# Semantic Search and Recommendations — Embeddings & Vector Search

Source: https://www.geekswithgeeks.com/en/embeddings/u-search

> Build search boxes and "similar items" features.

## Items near other items

**Semantic search**: embed all documents once, embed each query at request time, return the nearest items, ideally merged with keyword search and filtered by metadata. **"More like this"**: use an item's own vector as the query to find similar articles, products or support tickets. **Recommendations**: represent a user by the average (or a learned combination) of the vectors of items they liked, then fetch nearby items, remembering to **exclude** things already seen and to add **diversity** and business rules (stock, region, age restrictions). Cold-start items still get a vector from their content, which is an advantage over methods that need interaction history. Measure with **click-through, conversion or task success**, not only offline similarity.

## Search, recommend, classify, retrieve

The same vectors power search, recommendations, classification and retrieval for language models.

![Four uses: search, recommend, classify, ground.](assets/figures/embeddings/section-7-map.svg) — Figure 7.1 — Search, recommend, classify and ground.

## Exclude what was already seen

For recommendations, remove items the user already viewed or bought before showing nearest neighbours.

**Quiz:** How can an item's vector be used for "more like this"?

- [ ] Convert it back to the original text
- [ ] Delete it
- [x] Use it as the query to fetch its nearest neighbours
- [ ] Add it to the model

*Answer:* Use it as the query to fetch its nearest neighbours. An item is a point in the space; its neighbours are similar items.