  Bipartite ranking is a fundamental ranking problem that learns to order
relevant instances ahead of irrelevant ones. The pair-wise approach for
bi-partite ranking construct a quadratic number of pairs to solve the problem,
which is infeasible for large-scale data sets. The point-wise approach, albeit
more efficient, often results in inferior performance. That is, it is difficult
to conduct bipartite ranking accurately and efficiently at the same time. In
this paper, we develop a novel active sampling scheme within the pair-wise
approach to conduct bipartite ranking efficiently. The scheme is inspired from
active learning and can reach a competitive ranking performance while focusing
only on a small subset of the many pairs during training. Moreover, we propose
a general Combined Ranking and Classification (CRC) framework to accurately
conduct bipartite ranking. The framework unifies point-wise and pair-wise
approaches and is simply based on the idea of treating each instance point as a
pseudo-pair. Experiments on 14 real-word large-scale data sets demonstrate that
the proposed algorithm of Active Sampling within CRC, when coupled with a
linear Support Vector Machine, usually outperforms state-of-the-art point-wise
and pair-wise ranking approaches in terms of both accuracy and efficiency.
