short-paper

Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns

Authors:

Rick Siow Mong GohAuthors Info & Claims

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

Article No.: 12, Pages 1 - 4

https://doi.org/10.1145/2813448.2813521

Published: 16 September 2015 Publication History

Abstract

In our study, we investigate the effectiveness of different models to the purchasing behaviour at YOOCHOOSE website. This paper provide a direct method in modeling the buying pattern in a clicking session by simply using the time-stamp of the clicks and show that the result is comparable to using more massive feature engineering that requires session summarizing. Our proposed method requires much lesser feature engineering and more natural modeling of the click events directly in a typical purchasing session in e-commerce.

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Cited By

Adhikari J(2023)Online Shoppers' Purchase Intention using Ensemble Learning ApproachInternational Journal of Next-Generation Computing10.47164/ijngc.v14i4.1065Online publication date: 28-Nov-2023
https://doi.org/10.47164/ijngc.v14i4.1065
Alves Gomes MWönkhaus MMeisen PMeisen T(2023)TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer BehaviorJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1803007018:3(1404-1418)Online publication date: 17-Aug-2023
https://doi.org/10.3390/jtaer18030070
Nishimura NSukegawa NTakano YIwanaga J(2023)Predicting Online Item-Choice Behavior: A Shape-Restricted Regression ApproachAlgorithms10.3390/a1609041516:9(415)Online publication date: 29-Aug-2023
https://doi.org/10.3390/a16090415
Show More Cited By

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Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns

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cover image ACM Conferences

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

September 2015

53 pages

ISBN:9781450336659

DOI:10.1145/2813448

Copyright © 2015 ACM.

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 the author(s) 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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Published: 16 September 2015

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Short-paper
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Refereed limited

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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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Citations

Cited By

Adhikari J(2023)Online Shoppers' Purchase Intention using Ensemble Learning ApproachInternational Journal of Next-Generation Computing10.47164/ijngc.v14i4.1065Online publication date: 28-Nov-2023
https://doi.org/10.47164/ijngc.v14i4.1065
Alves Gomes MWönkhaus MMeisen PMeisen T(2023)TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer BehaviorJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1803007018:3(1404-1418)Online publication date: 17-Aug-2023
https://doi.org/10.3390/jtaer18030070
Nishimura NSukegawa NTakano YIwanaga J(2023)Predicting Online Item-Choice Behavior: A Shape-Restricted Regression ApproachAlgorithms10.3390/a1609041516:9(415)Online publication date: 29-Aug-2023
https://doi.org/10.3390/a16090415
Sudhakara MK. R. BKumar MBadrinath NRangaswamy K(2022)Customer Purchase Prediction and Potential Customer Identification for Digital Marketing Using Machine LearningAI-Driven Intelligent Models for Business Excellence10.4018/978-1-6684-4246-3.ch006(95-111)Online publication date: 12-Aug-2022
https://doi.org/10.4018/978-1-6684-4246-3.ch006
Alves Gomes MMeyes RMeisen PMeisen TAl Hasan MXiong L(2022)Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using EmbeddingsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557127(2873-2882)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557127
Puustinen PStefanidis KKekäläinen JJunkkari M(2021)Reinforcement Learning Page Prediction for Hierarchically Ordered Municipal WebsitesInformation10.3390/info1206023112:6(231)Online publication date: 28-May-2021
https://doi.org/10.3390/info12060231
Roychowdhury SAlareqi ELi W(2021)OPAM: Online Purchasing-behavior Analysis using Machine learning2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533658(1-8)Online publication date: 2021
https://doi.org/10.1109/IJCNN52387.2021.9533658
Li LSun LWeng CHuo CRen Wd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Spending Money WiselyProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412745(2597-2604)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412745
Requena BCassani GTagliabue JGreco CLacasa L(2020)Shopper intent prediction from clickstream e-commerce data with minimal browsing informationScientific Reports10.1038/s41598-020-73622-y10:1Online publication date: 12-Oct-2020
https://doi.org/10.1038/s41598-020-73622-y
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
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