I am working on a ranking problem to predict the right single document based on the user query and use the NDCG metric to measure the model.

Given the details :

Queries ( Q ), Result Document ( D ), Relevance score. But the relevance score is a binary ( 0 or 1 ) i.e out of document lists, only one document is marked as relevance score =1.

Data set example:

 query, docs,relevance
[1, doc2,0],[1, doc3,0],[1, doc4,0 ],[1, doc6,1],[1, doc9,0]
[2, doc3,0],[2, doc5,1],[2, doc10,0],[2, doc11,0],[2, doc1,0]

My questions: 1. Is it possible to use the NDCG metric for binary relevance problems? 2. If so, please share some reading notes or suggestions.



The nDCG depends on the relevance of each document as you can see on the Wikipedia definition. I guess you could use 0 and 1 as relevance scores, but then all relevant documents would have the same score of 1, and then it wouldn't make much sense to apply the nDCG penalty discounts.

A similar measure often used with binary relevance scores is the mean average precision defined as:


where $Q$ is the number of queries.

A comprehensive explanation of both nDCG and MAP is available here

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