Combining evidence from multiple retrieval models has been widely studied in the context of of distributed search, metasearch and rank fusion. Much of the prior work has focused on combining retrieval scores (or the rankings) assigned by different ...
In most existing works, nDCG is computed for a fixed cutoff k, i.e., nDCG@k and some fixed discounting coefficient. Such a conventional query-independent way to compute nDCG does not accurately reflect the utility of search results perceived by an ...
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficulty in obtaining reliable relevance judgments from human assessors when ...
Association for Computing Machinery
New York, NY, United States
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