Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees

Dato, Domenico; Lucchese, Claudio; Nardini, Franco Maria; Orlando, Salvatore; Perego, Raffaele; Tonellotto, Nicola; Venturini, Rossano

doi:10.1145/2987380

Cited by 71 publications

(66 citation statements)

References 25 publications

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“…The QUICKSCORER (QS) algorithm [15], [16], which restructures the data layout and the processing of an ensemble of regression trees to leverage modern memory hierarchies and reduce branch prediction errors to limit control hazards, resulted up to 6.6x faster than VPRED.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the complexity of QS in the worst case is linear time, i.e., Θ(Λ|T |) time. However, experiments on real datasets [16] show that the number of false nodes visited per tree is only a small fraction of Λ, and, admittedly, QS outperforms other algorithms with better worst-case time complexity.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we investigate multi/many-core parallelization strategies for making the traversal of ensembles fast and scalable. In particular, we investigate diverse strategies to parallelize QUICKSCORER (QS), the state-ofthe-art algorithm for traversing tree ensembles [15], [16] (we present QUICKSCORER in Section 2). Making faster QS allows the deployment of very large, complex, and effective ML models, by still producing the final ranking of a set of documents within a fixed small time budget.…”

Section: Introductionmentioning

confidence: 99%

“…Alg. 1 illustrates the QS [15], [16] algorithm for the fast traversal of the ensemble. The algorithm restructures the 2.…”

Section: Introductionmentioning

confidence: 99%

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Parallel Traversal of Large Ensembles of Decision Trees

Lettich

Lucchese

Nardini

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

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Machine-learnt models based on additive ensembles of regression trees are currently deemed the best solution to address complex classification, regression, and ranking tasks. The deployment of such models is computationally demanding: to compute the final prediction, the whole ensemble must be traversed by accumulating the contributions of all its trees. In particular, traversal cost impacts applications where the number of candidate items is large, the time budget available to apply the learnt model to them is limited, and the users' expectations in terms of quality-of-service is high. Document ranking in web search, where sub-optimal ranking models are deployed to find a proper trade-off between efficiency and effectiveness of query answering, is probably the most typical example of this challenging issue. This paper investigates multi/many-core parallelization strategies for speeding up the traversal of large ensembles of regression trees thus obtaining machine-learnt models that are, at the same time, effective, fast, and scalable. Our best results are obtained by the GPU-based parallelization of the state-of-the-art algorithm, with speedups of up to 102.6x.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Alg. 1 illustrates the QS [15], [16] algorithm for the fast traversal of the ensemble. The algorithm restructures the 2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallel Traversal of Large Ensembles of Decision Trees

Lettich

Lucchese

Nardini

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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“…Our experiments are performed over three large publicly available LTR datasets [4,7,23]; these are retired validations sets published by large commercial search engines. Each dataset consists of queries, documents and relevance labels.…”

Section: Datasetsmentioning

confidence: 99%

Ranking for Relevance and Display Preferences in Complex Presentation Layouts

Oosterhuis

Rijke

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Learning to Rank has traditionally considered settings where given the relevance information of objects, the desired order in which to rank the objects is clear. However, with today's large variety of users and layouts this is not always the case. In this paper, we consider socalled complex ranking settings where it is not clear what should be displayed, that is, what the relevant items are, and how they should be displayed, that is, where the most relevant items should be placed. These ranking settings are complex as they involve both traditional ranking and inferring the best display order. Existing learning to rank methods cannot handle such complex ranking settings as they assume that the display order is known beforehand. To address this gap we introduce a novel Deep Reinforcement Learning method that is capable of learning complex rankings, both the layout and the best ranking given the layout, from weak reward signals. Our proposed method does so by selecting documents and positions sequentially, hence it ranks both the documents and positions, which is why we call it the Double-Rank Model (DRM). Our experiments show that DRM outperforms all existing methods in complex ranking settings, thus it leads to substantial ranking improvements in cases where the display order is not known a priori.

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Optimizing Ranking Models in an Online Setting

Oosterhuis

Rijke

2019

Lecture Notes in Computer Science

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Online Learning to Rank (OLTR) methods optimize ranking models by directly interacting with users, which allows them to be very efficient and responsive. All OLTR methods introduced during the past decade have extended on the original OLTR method: Dueling Bandit Gradient Descent (DBGD). Recently, a fundamentally different approach was introduced with the Pairwise Differentiable Gradient Descent (PDGD) algorithm. To date the only comparisons of the two approaches are limited to simulations with cascading click models and low levels of noise. The main outcome so far is that PDGD converges at higher levels of performance and learns considerably faster than DBGD-based methods. However, the PDGD algorithm assumes cascading user behavior, potentially giving it an unfair advantage. Furthermore, the robustness of both methods to high levels of noise has not been investigated. Therefore, it is unclear whether the reported advantages of PDGD over DBGD generalize to different experimental conditions. In this paper, we investigate whether the previous conclusions about the PDGD and DBGD comparison generalize from ideal to worst-case circumstances. We do so in two ways. First, we compare the theoretical properties of PDGD and DBGD, by taking a critical look at previously proven properties in the context of ranking. Second, we estimate an upper and lower bound on the performance of methods by simulating both ideal user behavior and extremely difficult behavior, i.e., almost-random non-cascading user models. Our findings show that the theoretical bounds of DBGD do not apply to any common ranking model and, furthermore, that the performance of DBGD is substantially worse than PDGD in both ideal and worst-case circumstances. These results reproduce previously published findings about the relative performance of PDGD vs. DBGD and generalize them to extremely noisy and non-cascading circumstances.

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Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees

Cited by 71 publications

References 25 publications

Parallel Traversal of Large Ensembles of Decision Trees

Parallel Traversal of Large Ensembles of Decision Trees

Ranking for Relevance and Display Preferences in Complex Presentation Layouts

Optimizing Ranking Models in an Online Setting

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