Capannini G., Dato D., Lucchese C., Mori M., Nardini F. M., Orlando S., Perego R., Tonellotto N.
efficiency learning to rank big data
Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web collections where it involves effectiveness requirements and effciency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of effcient and effective Learning to Rank (LtR) algorithms that allows high-quality ranking functions to be devised from possibly huge training datasets. QuickRank is a project with a double goal: i) answering industrial need of Tiscali S.p.A. for a exible and scalable LtR solution for learning ranking models from huge training datasets; ii) providing the IR research community with a exible, extensible and effcient LtR framework to design LtR solutions and fairly compare the performance of different algorithms and ranking models. This paper presents our choices in designing QuickRank and report some preliminary use experiences.
Source: Italian Information Retrieval Workshop (IR), Cagliari, may 2015
Publisher: M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen., Aachen, Germania
@inproceedings{oai:it.cnr:prodotti:337566, title = {QuickRank: A C++ suite of learning to rank algorithms}, author = {Capannini G. and Dato D. and Lucchese C. and Mori M. and Nardini F. M. and Orlando S. and Perego R. and Tonellotto N.}, publisher = {M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen., Aachen, Germania}, booktitle = {Italian Information Retrieval Workshop (IR), Cagliari, may 2015}, year = {2015} }