QuickScorer: a Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees

Claudio Lucchese, HPC Lab., ISTI-CNR, Pisa, Italy
Franco Maria Nardini, HPC Lab., ISTI-CNR, Pisa, Italy
Salvatore Orlando, DAIS - Universit Ca’ Foscari Venezia, Italy
Raffaele Perego, HPC Lab., ISTI-CNR, Pisa, Italy
Nicola Tonellotto, HPC Lab., ISTI-CNR, Pisa, Italy
Rossano Venturini, University of Pisa

Apr. 17 2015

Accepted at the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago de Chile, 9–13 August 2015 [1].

Abstract. Learning-to-Rank models based on additive ensembles of regression 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 demanding. Unfortunately, the computational cost of these ranking 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 bitvector representation of the tree-based ranking model, and performs 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.

References

[1]   Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, and Rossano Venturini. Quickscorer: a fast algorithm to rank documents with additive ensembles of regression trees. In SIGIR ’15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015. (best paper) (ACM Notable Article).

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