To my understanding, they make an inter-query association based on the overlaps in relevant documents, e.g., q and p are two queries and they are related if R_q and R_p has intersections.
I'm thinking of developing a Jaccard Distance of two queries based on the set of their relevant docs. If it's more than a threshold, we consider them as the refined versions of each other.
How to calculate the pairwise Jaccard? I think it's close to calculate the number of pairwise collaborations like here
Brown University at TREC Deep Learning 2019:
To my understanding, they make an inter-query association based on the overlaps in relevant documents, e.g., q and p are two queries and they are related if R_q and R_p has intersections.
I'm thinking of developing a Jaccard Distance of two queries based on the set of their relevant docs. If it's more than a threshold, we consider them as the refined versions of each other.
How to calculate the pairwise Jaccard? I think it's close to calculate the number of pairwise collaborations like here