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Learning Item Temporal Dynamics for Predicting Buying Sessions

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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  • (2023)Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream DataSystems10.3390/systems1105025511:5(255)Online publication date: 18-May-2023
  • (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
  • (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
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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
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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Author Tags

  1. electronic commerce
  2. imbalanced data set
  3. machine learning
  4. recommender systems
  5. temporal dynamics

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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

View all
  • (2023)Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream DataSystems10.3390/systems1105025511:5(255)Online publication date: 18-May-2023
  • (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
  • (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
  • (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
  • (2019)The Dynamics of Online Consumers’ Response to Price PromotionInformation Systems Research10.1287/isre.2018.079330:1(175-190)Online publication date: Mar-2019
  • (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
  • (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
  • (2019)About timeJournal of Systems and Information Technology10.1108/JSIT-06-2017-0042Online publication date: 21-Jun-2019
  • (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
  • (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
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