Hi @xenawp314 ! Welcome to the Elastic community!
Question 1):
Relevance Tuning retrieves the final scores using weighted and boosted queries, depending on:
- Weight for the different fields
- Boosts for the field values
Besides that, score is naturally impacted by the significance of a term (how many times it appears in the document, how many in all the documents, etc).
Scoring is complex and can sometimes be counterintuitive. We would need to take a look into your data to understand what is causing a specific score. But from your description, probably having very different weights for product category will "shadow" the score obtained from the product title. I'd suggest using a more balanced approach; it will also benefit users who are looking for the exact product title instead of a category.
The advantage of the Query Tester is that you can check in real time what the impact of weights and boosts is for the query.
As a suggestion, you may add product category as a filter field to help users categorise the search results they get instead of relying on a direct search on the product category.
Search Relevance Tuning is part science, part art. It's difficult to fit all your use cases; it's important that you use App Search analytics to understand what the customers are looking for and what the expected results are, as well as business knowledge to prioritise and curate the search results.
Question 2:
When a specific product has the word repeated multiple times in a field, and the others not, it will be more relevant. The score will be impacted by the relative frequency of the term in that field for other documents.
I'd suggest to manually curate those product titles so they don't have as many repetitions. Maybe that description would fit into multiple fields:
- Product Title: Rice Chex
- Product Category: Rice Cereal
- Allergies: Gluten Free
- Ingredients: Rice
That way, you can provide a much richer search experience for the users (they can have multiple categories to search for, or drill down their search) as well as providing more accurate results.
Let us know about any other questions you might have!