Róbert Pálovics, Peter Szalai, Levente Kocsis, A. Szabó, Erzsébet Frigó, Júlia Pap, Zsófia K. Nyikes, A. Benczúr
{"title":"Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization","authors":"Róbert Pálovics, Peter Szalai, Levente Kocsis, A. Szabó, Erzsébet Frigó, Júlia Pap, Zsófia K. Nyikes, A. Benczúr","doi":"10.1145/2813448.2813513","DOIUrl":null,"url":null,"abstract":"The RecSys Challenge 2015 task requested prediction for items purchased in online web shop sessions. We describe our method that reached fifth place on the leaderboard by constructing a large number of item, session, and session-item features and using linear models and gradient boosted trees for learning. An important element of our method included optimization for the specific evaluation metric.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 International ACM Recommender Systems Challenge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2813448.2813513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The RecSys Challenge 2015 task requested prediction for items purchased in online web shop sessions. We describe our method that reached fifth place on the leaderboard by constructing a large number of item, session, and session-item features and using linear models and gradient boosted trees for learning. An important element of our method included optimization for the specific evaluation metric.