PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Dato D., Lucchese C., Nardini F. M., Orlando S., Perego R., Tonellotto N., Venturini R. Ranking documents efficiently with QuickScorer. In: SEDB 2016 - 24th Italian Symposium on Advanced Database Systems (Ugento, Lecce, 19-22 June 2016). Atti, pp. 166 - 173. Matematicamente.it, 2016.
 
 
Abstract
(English)
This discussion paper presents our recent work on the efficiency of Learning-to-Rank models based on additive ensembles of regression trees. These models, although computationally expensive, have proven to provide a very effective solution to the problem of ranking query results returned by Web search engines, a scenario where quality and efficiency requirements are very demanding. QS (qs), our novel scoring algorithm, 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. 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, qs performance are impressive, resulting in speedups 2x to 6.5x over state-of-the-art competitors. The paper proposing qs was awarded best paper at last ACM SIGIR conference.
URL: http://sebd2016.unisalento.it
Subject Learning to rank
Efficient scoring
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval


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