Document selection methodologies for efficient and effective learning-to-rank

Learning-to-rank has attracted great attention in the IR community. Much thought and research has been placed on query-document feature extraction and development of sophisticated learning-to-rank algorithms. However, relatively little research has been conducted on selecting documents for learning-to-rank data sets nor on the effect of these choices on the efficiency and effectiveness of learning-to-rank algorithms. In this paper, we employ a number of document selection methodologies, widely used in the context of evaluation--depth-k pooling, sampling (infAP, statAP), active-learning (MTC), and on-line heuristics (hedge). Certain methodologies, e.g. sampling and active-learning, have been shown to lead to efficient and effective evaluation. We investigate whether they can also enable efficient and effective learning-to-rank. We compare them with the document selection methodology used to create the …