 
Summary: The Infinite Push: A New Support Vector Ranking Algorithm that Directly
Optimizes Accuracy at the Absolute Top of the List
Shivani Agarwal
Abstract
Ranking problems have become increasingly important
in machine learning and data mining in recent years,
with applications ranging from information retrieval and
recommender systems to computational biology and drug
discovery. In this paper, we describe a new ranking
algorithm that directly maximizes the number of relevant
objects retrieved at the absolute top of the list. The
algorithm is a support vector style algorithm, but due to
the different objective, it no longer leads to a quadratic
programming problem. Instead, the dual optimization
problem involves l1, constraints; we solve this dual prob
lem using the recent l1, projection method of Quattoni
et al (2009). Our algorithm can be viewed as an lnorm
extreme of the lpnorm based algorithm of Rudin (2009)
(albeit in a support vector setting rather than a boosting
setting); thus we refer to the algorithm as the `Infinite
