QuickScorer: a fast algorithm to rank documents with additive ensembles of regression trees

Learning-to-Rank models based on additive ensembles of re- gression trees have proven to be very effective for ranking query results returned by Web search engines, a scenario where quality and efficiency requirements are very demand- ing. Unfortunately, the computational cost of these rank- ing models is high. Thus, several works already proposed solutions aiming at improving the efficiency of the scoring process by dealing with features and peculiarities of modern CPUs and memory hierarchies. In this paper, we present QuickScorer, a new algorithm that adopts a novel bitvec- tor representation of the tree-based ranking model, and per- forms an interleaved traversal of the ensemble by means of simple logical bitwise operations. The performance of the proposed algorithm are unprecedented, due to its cache- aware approach, both in terms of data layout and access patterns, and to a control flow that entails very low branch mis-prediction rates. The experiments on real Learning-to- Rank datasets show that QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x.