research-article

Learning Item Temporal Dynamics for Predicting Buying Sessions

Authors:

Veronika Bogina,

Osnat MokrynAuthors Info & Claims

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

Pages 251 - 255

https://doi.org/10.1145/2856767.2856781

Published: 07 March 2016 Publication History

Abstract

Predicting whether a session is a buying session (e.g. will end with buying an item) is an ongoing research task. Drawing from recent experience in Web search and movie recommenders, we explore the effect of temporal trends and characteristics on the ability to predict buying sessions. We suggest a new approach, based on items' temporal dynamics, together with sessions' temporal aspects for predicting whether a session is going to end up with a purchase. We suggest a model for estimating the probability of a session to end with a purchase, according to the purchase history of items clicked on during the session over the past few days. The predictions can be used by recommender systems, enabling them to take relevant actions, thus improving shoppers experience as well as increasing sales for e-commerce companies. Our findings shed light on the importance of considering temporal dynamics in items recommendations in e-commerce sites. Empirical results on imbalanced e-commerce dataset with more than nine million sessions demonstrate that we achieve high Precision, Recall and ROC in predicting whether session ends up with a purchase or not.

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Cited By

Wen ZLin WLiu H(2023)Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream DataSystems10.3390/systems1105025511:5(255)Online publication date: 18-May-2023
https://doi.org/10.3390/systems11050255
Bogina VKuflik TJannach DBielikova MKompan MTrattner C(2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
https://doi.org/10.1007/s11257-022-09335-w
Kaushik RSimmons R(2021)Early Prediction of Student Engagement-Related Events from Facial and Contextual FeaturesSocial Robotics10.1007/978-3-030-90525-5_26(308-318)Online publication date: 2-Nov-2021
https://doi.org/10.1007/978-3-030-90525-5_26
Show More Cited By

Index Terms

Learning Item Temporal Dynamics for Predicting Buying Sessions
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

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Published In

cover image ACM Conferences

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

March 2016

446 pages

ISBN:9781450341370

DOI:10.1145/2856767

General Chairs:
Jeffrey Nichols
Google Inc, USA
,
Jalal Mahmud
IBM Research, USA
,
John O'Donovan
UC Santa Barbara, USA
,
Program Chairs:
Cristina Conati
University of British Columbia, Canada
,
Massimo Zancanaro
Bruno Kessler Foundation, Italy

Copyright © 2016 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 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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Published: 07 March 2016

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IUI'16: 21st International Conference on Intelligent User Interfaces

March 7 - 10, 2016

California, Sonoma, USA

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IUI '16 Paper Acceptance Rate 49 of 194 submissions, 25%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Wen ZLin WLiu H(2023)Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream DataSystems10.3390/systems1105025511:5(255)Online publication date: 18-May-2023
https://doi.org/10.3390/systems11050255
Bogina VKuflik TJannach DBielikova MKompan MTrattner C(2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
https://doi.org/10.1007/s11257-022-09335-w
Kaushik RSimmons R(2021)Early Prediction of Student Engagement-Related Events from Facial and Contextual FeaturesSocial Robotics10.1007/978-3-030-90525-5_26(308-318)Online publication date: 2-Nov-2021
https://doi.org/10.1007/978-3-030-90525-5_26
Esmeli RBader-El-Den MAbdullahi H(2020)Using Word2Vec Recommendation for Improved Purchase Prediction2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9206871(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9206871
Kim YKrishnan R(2019)The Dynamics of Online Consumers’ Response to Price PromotionInformation Systems Research10.1287/isre.2018.079330:1(175-190)Online publication date: Mar-2019
https://doi.org/10.1287/isre.2018.0793
Lin ZChen HAkerkar RJung J(2019)A Probabilistic Model for Collaborative FilteringProceedings of the 9th International Conference on Web Intelligence, Mining and Semantics10.1145/3326467.3326472(1-8)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1145/3326467.3326472
Esmeli RBader-El-Den MMohasseb A(2019)Context and Short Term User Intention Aware Hybrid Session Based Recommendation System2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)10.1109/INISTA.2019.8778352(1-6)Online publication date: Jul-2019
https://doi.org/10.1109/INISTA.2019.8778352
Salonen VKarjaluoto H(2019)About timeJournal of Systems and Information Technology10.1108/JSIT-06-2017-0042Online publication date: 21-Jun-2019
https://doi.org/10.1108/JSIT-06-2017-0042
Lin ZChen H(2019)Recommendation over time: a probabilistic model of time-aware recommender systemsScience China Information Sciences10.1007/s11432-018-9915-862:11Online publication date: 9-Oct-2019
https://doi.org/10.1007/s11432-018-9915-8
N CRavindran BRanu SGanguly NRamakrishnan RSarawagi SRoy S(2018)A neural attention based approach for clickstream miningProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3152494.3152505(118-127)Online publication date: 11-Jan-2018
https://dl.acm.org/doi/10.1145/3152494.3152505
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