@conference {15310,
	title = {Learning to efficiently rank},
	booktitle = {Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval},
	series = {SIGIR {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {138 - 145},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {It has been shown that learning to rank approaches are capable of learning highly effective ranking functions. However, these approaches have mostly ignored the important issue of efficiency. Given that both efficiency and effectiveness are important for real search engines, models that are optimized for effectiveness may not meet the strict efficiency requirements necessary to deploy in a production environment. In this work, we present a unified framework for jointly optimizing effectiveness and efficiency. We propose new metrics that capture the tradeoff between these two competing forces and devise a strategy for automatically learning models that directly optimize the tradeoff metrics. Experiments indicate that models learned in this way provide a good balance between retrieval effectiveness and efficiency. With specific loss functions, learned models converge to familiar existing ones, which demonstrates the generality of our framework. Finally, we show that our approach naturally leads to a reduction in the variance of query execution times, which is important for query load balancing and user satisfaction.},
	keywords = {effectiveness and efficiency tradeoff, Learning to rank, Linear Models},
	isbn = {978-1-4503-0153-4},
	doi = {10.1145/1835449.1835475},
	url = {http://doi.acm.org/10.1145/1835449.1835475},
	author = {Wang,Lidan and Jimmy Lin and Metzler,Donald}
}
@conference {15302,
	title = {Ranking under temporal constraints},
	booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management},
	series = {CIKM {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {79 - 88},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This paper introduces the notion of temporally constrained ranked retrieval, which, given a query and a time constraint, produces the best possible ranked list within the specified time limit. Naturally, more time should translate into better results, but the ranking algorithm should always produce some results. This property is desirable from a number of perspectives: to cope with diverse users and information needs, as well as to better manage system load and variance in query execution times. We propose two temporally constrained ranking algorithms based on a class of probabilistic prediction models that can naturally incorporate efficiency constraints: one that makes independent feature selection decisions, and the other that makes joint feature selection decisions. Experiments on three different test collections show that both ranking algorithms are able to satisfy imposed time constraints, although the joint model outperforms the independent model in being able to deliver more effective results, especially under tight time constraints, due to its ability to capture feature dependencies.},
	keywords = {efficiency, Learning to rank, Linear Models},
	isbn = {978-1-4503-0099-5},
	doi = {10.1145/1871437.1871452},
	url = {http://doi.acm.org/10.1145/1871437.1871452},
	author = {Wang,Lidan and Metzler,Donald and Jimmy Lin}
}
