{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:01:26Z","timestamp":1768590086432,"version":"3.49.0"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2018,10,29]],"date-time":"2018-10-29T00:00:00Z","timestamp":1540771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"crossref","award":["777695,780751,654024"],"award-info":[{"award-number":["777695,780751,654024"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,11,30]]},"abstract":"<jats:p>\n            Learning-to-Rank (LtR) solutions are commonly used in large-scale information retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradient boosting approach to build additive ensembles of weighted regression trees. Since the required ranking effectiveness is achieved with very large ensembles, the impact on response time and query throughput of these solutions is not negligible. In this article, we propose X-CLE\n            <jats:sc>a<\/jats:sc>\n            VER, an iterative meta-algorithm able to build more efficient and effective ranking ensembles. X-CLE\n            <jats:sc>a<\/jats:sc>\n            VER interleaves the iterations of a given gradient boosting learning algorithm with pruning and re-weighting phases. First, redundant trees are removed from the given ensemble, then the weights of the remaining trees are fine-tuned by optimizing the desired ranking quality metric. We propose and analyze several pruning strategies and we assess their benefits showing that interleaving pruning and re-weighting phases during learning is more effective than applying a single post-learning optimization step. Experiments conducted using two publicly available LtR datasets show that X-CLE\n            <jats:sc>a<\/jats:sc>\n            VER can be successfully exploited on top of several LtR algorithms as it is effective in optimizing the effectiveness of the learnt ensembles, thus obtaining more compact forests that hence are much more efficient at scoring time.\n          <\/jats:p>","DOI":"10.1145\/3205453","type":"journal-article","created":{"date-parts":[[2018,10,29]],"date-time":"2018-10-29T12:02:18Z","timestamp":1540814538000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["X-CLE\n            <scp>a<\/scp>\n            VER"],"prefix":"10.1145","volume":"9","author":[{"given":"Claudio","family":"Lucchese","sequence":"first","affiliation":[{"name":"Ca\u2019 Foscari University of Venice, Dorsoduro, Venice, Italy"}]},{"given":"Franco Maria","family":"Nardini","sequence":"additional","affiliation":[{"name":"ISTI-CNR, Via Moruzzi, Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6541-9409","authenticated-orcid":false,"given":"Salvatore","family":"Orlando","sequence":"additional","affiliation":[{"name":"Ca\u2019 Foscari University of Venice, Dorsoduro, Venice, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7189-4724","authenticated-orcid":false,"given":"Raffaele","family":"Perego","sequence":"additional","affiliation":[{"name":"ISTI-CNR, Via Moruzzi, Pisa, Italy"}]},{"given":"Fabrizio","family":"Silvestri","sequence":"additional","affiliation":[{"name":"ISTI-CNR, Via Moruzzi, Pisa, Italy"}]},{"given":"Salvatore","family":"Trani","sequence":"additional","affiliation":[{"name":"ISTI-CNR, Via Moruzzi, Pisa, Italy"}]}],"member":"320","published-online":{"date-parts":[[2018,10,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36973-5_13"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.73"},{"key":"e_1_2_1_3_1","volume-title":"Baeza-Yates","author":"Cambazoglu Berkant Barla","year":"2015","unstructured":"Berkant Barla Cambazoglu and Ricardo A . Baeza-Yates . 2015 . Scalability Challenges in Web Search Engines. Morgan 8 Claypool Publishers . Berkant Barla Cambazoglu and Ricardo A. Baeza-Yates. 2015. Scalability Challenges in Web Search Engines. Morgan 8 Claypool Publishers."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718538"},{"key":"e_1_2_1_5_1","volume-title":"Salvatore Orlando, Raffaele Perego, and Nicola Tonellotto.","author":"Capannini Gabriele","year":"2015","unstructured":"Gabriele Capannini , Domenico Dato , Claudio Lucchese , Monica Mori , Franco Maria Nardini , Salvatore Orlando, Raffaele Perego, and Nicola Tonellotto. 2015 . QuickRank: A C++ suite of learning to rank algorithms. Retrieved from http:\/\/quickrank.isti.cnr.it\/ (2018) IIR'15: 6th Italian Information Retrieval Workshop Gabriele Capannini, Domenico Dato, Claudio Lucchese, Monica Mori, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, and Nicola Tonellotto. 2015. QuickRank: A C++ suite of learning to rank algorithms. Retrieved from http:\/\/quickrank.isti.cnr.it\/ (2018) IIR'15: 6th Italian Information Retrieval Workshop"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2016.05.004"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS\u201909)","author":"Chen Wei","year":"2009","unstructured":"Wei Chen , Tie-Yan Liu , Yanyan Lan , Zhiming Ma , and Hang Li . 2009 . Ranking measures and loss functions in learning to rank . In Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS\u201909) . 315--323. Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, and Hang Li. 2009. Ranking measures and loss functions in learning to rank. 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