Understanding Rank Fusion Score in Typesense's Multi-Search

Hei there - We're currently implementing multi-search with hybrid/semantic search using Typesense. I have a question regarding the rank_fusion_score in different collections. Can these scores be compared across various collections?

Further is there a recommend or existing way to combine across collections ( mostly by with shared properties) then rank them using either rank_fusion_score or vector distance ?

Rank fusion score combines positional information between semantic and keyword search. So technically you can compare them across two searches in two collections.

Thanks Kishore Nallan you for your initial response.

To provide further clarity on the second question, let's consider two collections: Collection A and its sub-collection, Collection B.

Our objective is to establish a ranking system utilizing the rank_fusion_score.
In essence, we aim to rank a document in Collection A based on both its inherent rank_fusion_score and the cumulative rank_fusion_score of all the document hits present in Collection B that are linked to the corresponding document in Collection A.

The question arises: Should we simply aggregate the rank_fusion_score values from the hits in Collection B that are associated with the specific document in Collection A? Or is there a distinct approach for merging and subsequently ranking these scores?

Extending on the first question the rank_fusion_score are based on the semantic and keyword ranks [Source]? How are they calculated are they just the indexes in the corresponding search hits ?

Or are ranks and scores used interchangeably?

Rank fusion score is computed purely on the position of a hit within the results in semantic and keyword search hits. Higher the hit on the list, the higher the score. We assign 70% weight for keyword search and 30% weight-age for semantic search. So you should be able to compare/combine scores across collections along with your own weights for each collection.