Collection construction methodologies for learning to rank

• Online Access: http://hdl.handle.net/2047/d20002915

Ranking documents in response to user queries is one of the fundamental problems in Information Retrieval. Learning to Rank has emerged as an effective approach for data-driven construction of ranking algorithms. Although many algorithms have been created, the effect of the properties of the training data through which such algorithms are developed has not been systematically studied. The creation of a learning dataset requires great effort since every document in the dataset has to be manually labeled with its degree of relevance. Motivated by the importance of efficiently creating quality datasets, I study 1) the effect of characteristics of the training dataset on algorithm quality and 2) theoretically founded methods for construction of training datasets.