How To Use Best-Bets Labels to Map Query Terms

Profiles: Project Creator, Search Engineer

This page provides an example of how to use best-bet labels to mapy query terms.

Reference: Learn more about Document Relevancy.

Relying on the default relevancy score might be good enough for homogeneous datasets, where all the documents share a similar structure (document size) and/or come from the same source repository / domain.

But what if the data shares similar content, but is very different in terms of domain & structure?

Search Use Case Walk Through

In this example, a user wants to find a tutorial about a product, but doesn’t know how the product is exactly named nor what kind of information exists.

An example user queries for elastic search tutorial, and the initial result in that project is the following:

Why are the wrong documents ranked highest?

• The response contains many documents where search matches on the title (title matches have more weight per default), but none of the results are actually relevant.

  • Additionally those top ranked results have very short content, but contain matches on title and body (high score)

• The expected document with the title Learning Elasticsearch is not found

  • The terms elastic and search match, but the ebook contains a lot of text and overall the relevancy score is not high enough. This is partly the case because BM25 similarity scoring considers the document length and promotes shorter documents per default.

How can this be improved?

Several techniques can be applied to bring the correct answer to the top.

Here we show how searchable labels can be used to use the search tuning technique called Best Bets: Tag any document with additional content that you think Users are searching for in order to find the expected document.

As a project-creator, you can analyze the query behaviour of users to get a better understanding what keywords are mostly searched for.

Create Searchable Label Best Bets

Go to Data > Labels and create a new searchable Label. This label is used to store additional information used for document matching.

Configure a Scoring Profile for Customized Relevance Ranking

Note

Starting with Squirro 3.14.2, the recommended way of boosting documents that match a specific label in a more stable and impactful way is to use a scoring profile as shown below.

Example Scoring Profile for Best Bets:

Project Configuration topic.search.document-scoring-profiles

This is achieved by combining the scale_by function with the fulltext_match scoring plugin.

{
    "best_bet_boost": {
        "query": "scale_by:{profile:{fulltext_match text:{{query_terms}} fields:best_bet}}^20"
    }
}

This profile provides a 20x boost for documents where the user’s query terms match content in the best_bet label, offering more predictable and powerful boosting compared to traditional additive label-match based scoring.

For more details, see Advanced Usage: Relevance Ranking with Conditional Boosting.

Annotate Target Document

Tag the target document with keywords, phrases or alternate descriptions that are expected to match user queries (map additional user query vocabulary to the document – content which is not available on the document itself)

Tag document with expected keywords: elastic search tutorial guide

This is also beneficial to add synonyms scoped to one document only.

Result

For the same user, the top ranked document is now the expected Ebook.