Many real-world data mining tasks require the achievement of two distinct goals when applied to unseen data: first, to induce an accurate preference ranking, and second to give good regression performance. In this paper, we give an efficient and effective Combined Regression and Ranking method (CRR) that optimizes regression and ranking objectives simultaneously. We demonstrate the effectiveness of CRR for both families of metrics on a range of large-scale tasks, including click prediction for online advertisements. Results show that CRR often achieves performance equivalent to the best of both ranking-only and regression-only approaches. In the case of rare events or skewed distributions, we also find that this combination can actually improve regression performance due to the addition of informative ranking constraints.
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