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Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization

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András A. BenczúrAuthors Info & Claims

RecSys '15 Challenge: Proceedings of the 2015 International ACM Recommender Systems Challenge

Article No.: 4, Pages 1 - 4

https://doi.org/10.1145/2813448.2813513

Published: 16 September 2015 Publication History

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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.

References

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R. Pálovics, F. Ayala-Gómez, B. Csikota, B. Daróczy, L. Kocsis, D. Spadacene, and A. A. Benczúr. Recsys challenge 2014: an ensemble of binary classifiers and matrix factorization. In Proceedings of the 2014 Recommender Systems Challenge, page 13. ACM, 2014.

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Cited By

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Cao JCao J(2023)Application of Business Big Data Management and Decision MakingE-Commerce Big Data Mining and Analytics10.1007/978-981-99-3588-8_9(181-203)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-981-99-3588-8_9
Esmeli RBader-El-Den MAbdullahi H(2020)Towards early purchase intention prediction in online session based retailing systemsElectronic Markets10.1007/s12525-020-00448-xOnline publication date: 19-Dec-2020
https://doi.org/10.1007/s12525-020-00448-x
HOU CCHEN CWANG J(2018)Tree-Based Feature Transformation for Purchase Behavior PredictionIEICE Transactions on Information and Systems10.1587/transinf.2017EDL8210E101.D:5(1441-1444)Online publication date: 1-May-2018
https://doi.org/10.1587/transinf.2017EDL8210

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Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization

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RecSys Challenge 2015 and the YOOCHOOSE Dataset
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RecSys Challenge 2015 is about predicting the items a user will buy in a given click session. We describe the in-house solution to the challenge as guided by the YOOCHOOSE team. The presented solution achieved 14th place in the challenge's final ...

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Information

Published In

RecSys '15 Challenge: Proceedings of the 2015 International ACM Recommender Systems Challenge

September 2015

53 pages

ISBN:9781450336659

DOI:10.1145/2813448

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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Short-paper
Research
Refereed limited

Conference

RecSys '15

Sponsor:

SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

Acceptance Rates

RecSys '15 Challenge Paper Acceptance Rate 12 of 21 submissions, 57%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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Cao JCao J(2023)Application of Business Big Data Management and Decision MakingE-Commerce Big Data Mining and Analytics10.1007/978-981-99-3588-8_9(181-203)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-981-99-3588-8_9
Esmeli RBader-El-Den MAbdullahi H(2020)Towards early purchase intention prediction in online session based retailing systemsElectronic Markets10.1007/s12525-020-00448-xOnline publication date: 19-Dec-2020
https://doi.org/10.1007/s12525-020-00448-x
HOU CCHEN CWANG J(2018)Tree-Based Feature Transformation for Purchase Behavior PredictionIEICE Transactions on Information and Systems10.1587/transinf.2017EDL8210E101.D:5(1441-1444)Online publication date: 1-May-2018
https://doi.org/10.1587/transinf.2017EDL8210

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Recommendations

RecSys Challenge 2015 and the YOOCHOOSE Dataset

RecSys Challenge 2015: ensemble learning with categorical features

In-House Solution for the RecSys Challenge 2015