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Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce

Authors:

Huan LiuAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 365 - 374

https://doi.org/10.1145/3209978.3209993

Published: 27 June 2018 Publication History

Abstract

In recent years, product search engines have emerged as a key factor for online businesses. According to a recent survey, over 55% of online customers begin their online shopping journey by searching on an E-Commerce (EC) website like Amazon as opposed to a generic web search engine like Google. Information retrieval research to date has been focused on optimizing search ranking algorithms for web documents while little attention has been paid to product search. There are several intrinsic differences between web search and product search that make the direct application of traditional search ranking algorithms to EC search platforms difficult. First, the success of web and product search is measured differently; one seeks to optimize for relevance while the other must optimize for both relevance and revenue. Second, when using real-world EC transaction data, there is no access to manually annotated labels. In this paper, we address these differences with a novel learning framework for EC product search called LETORIF (LEarning TO Rank with Implicit Feedback). In this framework, we utilize implicit user feedback signals (such as user clicks and purchases) and jointly model the different stages of the shopping journey to optimize for EC sales revenue. We conduct experiments on real-world EC transaction data and introduce a a new evaluation metric to estimate expected revenue after re-ranking. Experimental results show that LETORIF outperforms top competitors in improving purchase rates and total revenue earned.

References

[1]

Kamelia Aryafar, Devin Guillory, and Liangjie Hong. 2017. An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. In Proceedings of the ADKDD'17. ACM, 10.

Digital Library

[2]

Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning. ACM, 89--96.

Digital Library

[3]

Christopher JC Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning 11, 23--581 (2010), 81.

[4]

Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning. ACM, 129--136.

Digital Library

[5]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST) 2, 3 (2011), 27.

Digital Library

[6]

Ovidiu Dan and Brian D Davison. 2016. Measuring and Predicting Search Engine Users Satisfaction. ACM Computing Surveys (CSUR) 49, 1 (2016), 18.

Digital Library

[7]

Huizhong Duan, ChengXiang Zhai, Jinxing Cheng, and Abhishek Gattani. 2013. Supporting keyword search in product database: a probabilistic approach. Proceedings of the VLDB Endowment 6, 14 (2013), 1786--1797.

Digital Library

[8]

Yoav Freund, Raj Iyer, Robert E Schapire, and Yoram Singer. 2003. An eicient boosting algorithm for combining preferences. Journal of machine learning research 4, Nov (2003), 933--969.

Digital Library

[9]

Yoav Freund and Robert E Schapire. 1995. A desicion-theoretic generalization of on-line learning and an application to boosting. In European conference on computational learning theory. Springer, 23--37.

Digital Library

[10]

Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.

[11]

Qi Guo and Eugene Agichtein. 2012. Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior. In Proceedings of the 21st international conference on World Wide Web. ACM, 569--578.

Digital Library

[12]

Liangjie Hong, Aziz S Doumith, and Brian D Davison. 2013. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 557--566.

Digital Library

[13]

Kalervo Järvelin and Jaana Kekäläinen. 2000. IR evaluation methods for retrieving highly relevant documents. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 41--48.

Digital Library

[14]

Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2017. Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum, Vol. 51. Acm, 4--11.

Digital Library

[15]

Solomon Kullback and Richard A Leibler. 1951. On information and suiciency. The annals of mathematical statistics 22, 1 (1951), 79--86.

[16]

Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller. 2012. Eicient backprop. In Neural networks: Tricks of the trade. Springer, 9--48.

[17]

Beibei Li, Anindya Ghose, and Panagiotis G Ipeirotis. 2011. Towards a theory model for product search. In Proceedings of the 20th international conference on World wide web. ACM, 327--336.

Digital Library

[18]

Ping Li, Qiang Wu, and Christopher J Burges. 2008. Mcrank: Learning to rank using multiple classiication and gradient boosting. In Advances in neural information processing systems. 897--904.

Digital Library

[19]

Andy Liaw, Matthew Wiener, et al. 2002. Classiication and regression by randomForest. R news 2, 3 (2002), 18--22.

[20]

Tie-Yan Liu. 2009. Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3, 3 (2009), 225--331.

Digital Library

[21]

Bo Long, Jiang Bian, Anlei Dong, and Yi Chang. 2012. Enhancing product search by best-selling prediction in e-commerce. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2479-2482.

Digital Library

[22]

Wendy W Moe. 2003. Buying, searching, or browsing: Diferentiating between online shoppers using in-store navigational clickstream. Journal of consumer psychology 13, 1--2 (2003), 29--39.

[23]

Tao Qin, Tie-Yan Liu, Jun Xu, and Hang Li. 2010. LETOR: A benchmark collection for research on learning to rank for information retrieval. Information Retrieval 13, 4 (2010), 346--374.

Digital Library

[24]

Filip Radlinski, Andrei Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, and Lance Riedel. 2008. Optimizing relevance and revenue in ad search: a query substitution approach. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 403--410.

Digital Library

[25]

Rómer Rosales, Haibin Cheng, and Eren Manavoglu. 2012. Post-click conversion modeling and analysis for non-guaranteed delivery display advertising. In Proceedings of the ifth ACM international conference on Web search and data mining. ACM, 293--302.

Digital Library

[26]

Shubhra Kanti Karmaker Santu, Parikshit Sondhi, and ChengXiang Zhai. 2017. On Application of Learning to Rank for E-Commerce Search. (2017).

[27]

Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka. 2008. Softrank: optimizing non-smooth rank metrics. In Proceedings of the 2008 International Conference on Web Search and Data Mining. ACM, 77--86.

Digital Library

[28]

Hamed Valizadegan, Rong Jin, Ruofei Zhang, and Jianchang Mao. 2009. Learning to rank by optimizing ndcg measure. In Advances in neural information processing systems. 1883--1891.

Digital Library

[29]

Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2016. Learning latent vector spaces for product search. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 165--174.

Digital Library

[30]

Damir Vandic, Flavius Frasincar, and Uzay Kaymak. 2013. Facet selection algorithms for web product search. In Proceedings of the 22nd ACM international conference on Conference on information&knowledge management. ACM, 2327- 2332.

Digital Library

[31]

Flavian Vasile, Damien Lefortier, and Olivier Chapelle. 2016. Cost-sensitive learning for utility optimization in online advertising auctions. arXiv preprint arXiv:1603.03713 (2016).

Digital Library

[32]

Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. 2016. Learning to rank with selection bias in personal search. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 115--124.

Digital Library

[33]

Liang Wu, Xia Hu, and Huan Liu. 2016. Relational learning with social status analysis. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 513--522.

Digital Library

[34]

Liang Wu, Jundong Li, Fred Morstatter, and Huan Liu. 2018. Toward Relational Learning with Misinformation. In Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM.

[35]

Qiang Wu, Christopher JC Burges, Krysta M Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Information Retrieval 13, 3 (2010), 254--270.

Digital Library

[36]

Jun Xu and Hang Li. 2007. Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 391--398.

Digital Library

[37]

Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. 2008. Directly optimizing evaluation measures in learning to rank. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 107--114.

Digital Library

[38]

Yanwu Yang, Yinghui Catherine Yang, Bernard J Jansen, and Mounia Lalmas. 2017. Computational Advertising: A Paradigm Shift for Advertising and Marketing? IEEE Intelligent Systems 32, 3 (2017), 3--6.

Digital Library

[39]

Dawei Yin, Yuening Hu, Jiliang Tang, Tim Daly, Mianwei Zhou, Hua Ouyang, Jianhui Chen, Changsung Kang, Hongbo Deng, Chikashi Nobata, et al. 2016. Ranking relevance in yahoo search. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 323- 332.

Digital Library

[40]

Jun Yu, Sunil Mohan, Duangmanee Pew Putthividhya, and Weng-Keen Wong. 2014. Latent dirichlet allocation based diversiied retrieval for e-commerce search. In Proceedings of the 7th ACM international conference on Web search and data mining. ACM, 463--472.

Digital Library

[41]

Weinan Zhang, Shuai Yuan, and Jun Wang. 2014. Optimal real-time bidding for display advertising. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1077--1086.

Digital Library

[42]

Qian Zhao, Yue Shi, and Liangjie Hong. 2017. GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1311--1319.

Digital Library

[43]

Yunzhang Zhu, Gang Wang, Junli Yang, Dakan Wang, Jun Yan, and Zheng Chen. 2009. Revenue optimization with relevance constraint in sponsored search. In Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising. ACM, 55--60.

Digital Library

[44]

Yunzhang Zhu, Gang Wang, Junli Yang, Dakan Wang, Jun Yan, Jian Hu, and Zheng Chen. 2009. Optimizing search engine revenue in sponsored search. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, 588--595.

Digital Library

Cited By

Yang GLi KZhang YFeng Q(2024)End-to-End Modeling and Long Short-Term Memory Application in Time Series ModelingJournal of Organizational and End User Computing10.4018/JOEUC.34973236:1(1-27)Online publication date: 30-Jul-2024
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Index Terms

Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce
1. Applied computing
  1. Electronic commerce
    1. Online shopping
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

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cover image ACM Conferences

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Copyright © 2018 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 27 June 2018

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SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

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SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://doi.org/10.4018/JOEUC.349732
Yang YChen BZhu CZhu MDai XGuo HZhang MDong ZTang R(2024)AIE: Auction Information Enhanced Framework for CTR Prediction in Online AdvertisingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688136(633-642)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688136
Tang XQiao YLyu FLiu DHe X(2024)Touch the Core: Exploring Task Dependence Among Hybrid Targets for RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688101(329-339)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688101
Zhang XHuang CZheng KSu HJi TWang WQi HLi JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Adversarial-Enhanced Causal Multi-Task Framework for Debiasing Post-Click Conversion Rate EstimationProceedings of the ACM Web Conference 202410.1145/3589334.3645379(3287-3296)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645379
Xia LHuang CXu YDai PBo L(2024)Multi-Behavior Graph Neural Networks for Recommender SystemIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.3204775(1-15)Online publication date: 2024
https://doi.org/10.1109/TNNLS.2022.3204775
Zhu QZhang HHe QDou Z(2024)Query-Aware Explainable Product Search With Reinforcement Knowledge Graph ReasoningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329733136:3(1260-1273)Online publication date: Mar-2024
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Yang XXu XXu ZChu JHu J(2024)DiffGCL: Diffusion model-based Graph Contrastive Learning for Service Recommendation2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00053(310-319)Online publication date: 7-Jul-2024
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Wu BCheng YYuan HMa Q(2024)When Multi-Behavior Meets Multi-Interest: Multi-Behavior Sequential Recommendation with Multi-Interest Self-Supervised Learning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00070(845-858)Online publication date: 13-May-2024
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Zheng YLi CDong JYu Y(2024)Globally Informed Graph Contrastive Learning for RecommendationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5618-6_23(274-286)Online publication date: 1-Aug-2024
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Zhou YFeng TLiu MZhu ZFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate EstimationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614760(3554-3563)Online publication date: 21-Oct-2023
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