I have a scenario where I have 2 sets of data:
- All possible products
- Products carried by a store
Dataset 2 is a subset of dataset 1.
If a user searches for a product that the store doesn’t carry, I’d like to be able to return similar products from the search. The problem is that product names don’t indicate what category/subcategory they belong in so right now I just get text-based search results.
So let’s say a user searches for a self-help book by name, but the store doesn’t carry it. I’d like to recommend other self-help books.
Conceptually, I’d be able to search dataset 1 to get all of the relevant information about the book and then use that to search dataset 2, but I’m having trouble approaching how I’d go about doing that.
Right now I have the datasets in OpenSearch and I’d like to use that as I intend to pass the search results through an Amazon Personalize campaign to re-rank the items for the user searching.