anchor.diff:  
   In contrast to the two metrics above, 
   https://en.wikipedia.org/wiki/Discounted_cumulative_gain[discounted cumulative gain] 
  -takes both, the rank and the rating of the search results, into account.
  +takes both the rank and the rating of the search results into account.
   
   The assumption is that highly relevant documents are more useful for the user 
   when appearing at the top of the result list. Therefore, the DCG formula reduces 
  
positive.code: search system.
  
  The challenge for search engineers is to tweak this translation process from 
  user entries to a concrete query in such a way, that the search results contain 
  the most relevant information with respect to the users information need. This 
  can only be done if the search result quality is evaluated constantly across a 
  representative test suite of typical user queries, so that improvements in the 
  rankings for one particular query doesn't negatively effect the ranking for 
  other types of queries.
  
  In order to get started with search quality evaluation, three basic things are 
  needed:
  
  . A collection of documents you want to evaluate your query performance against, 
    usually one or more indices.
  . A collection of typical search requests that users enter into your system.
  . A set of document ratings that judge the documents relevance with respect to a 
    search request.
    
  It is important to note that one set of document ratings is needed per test 
  query, and that the relevance judgements are based on the information need of 
  
negative.code: It is based on the assumption of a cascade model of search, in which a user 
  scans through ranked search results in order and stops at the first document 
  that satisfies the information need. For this reason, it is a good metric for 
  question answering and navigation queries, but less so for survey-oriented 
  information needs where the user is interested in finding many relevant 
  documents in the top k results.
  
  The metric models the expectation of the reciprocal of the position at which a 
  user stops reading through the result list. This means that a relevant document
  in a top ranking position will have a large contribution to the overall score. 
  However, the same document will contribute much less to the score if it appears 
  in a lower rank; even more so if there are some relevant (but maybe less relevant) 
  documents preceding it. In this way, the ERR metric discounts documents that 
  are shown after very relevant documents. This introduces a notion of dependency 
  in the ordering of relevant documents that e.g. Precision or DCG don't account 
  for.
  