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Linear and Non-Linear Models for Purchase Prediction

Authors:

Wenliang Chen,

Zhenghua Li,

Min ZhangAuthors Info & Claims

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

Article No.: 9, Pages 1 - 4

https://doi.org/10.1145/2813448.2813518

Published: 16 September 2015 Publication History

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Abstract

In this paper, we present our approach for the task of product purchase prediction. In the task, there are a collection of sequences of click events: click sessions. For some of the sessions, there are also buying events. The target of this task is to predict whether a user is going to buy something or not in a session, and if the user is buying, which products (items) the user is going to buy. In our approach, we treat the task as a classification problem and use linear and non-linear models to make the predictions, and then build an ensemble system based on the output of the individual systems. The evaluation results show that our final system is effective on the test data.

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Cited By

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Zhang WWang M(2021)An improved deep forest model for prediction of e-commerce consumers’ repurchase behaviorPLOS ONE10.1371/journal.pone.025590616:9(e0255906)Online publication date: 20-Sep-2021
https://doi.org/10.1371/journal.pone.0255906
Zhu BTang WMao XYang W(2020)Location-based Hybrid Deep Learning Model for Purchase Prediction2020 5th International Conference on Computational Intelligence and Applications (ICCIA)10.1109/ICCIA49625.2020.00038(161-165)Online publication date: Jun-2020
https://doi.org/10.1109/ICCIA49625.2020.00038
Dou X(2020)Online Purchase Behavior Prediction and Analysis Using Ensemble Learning2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA49378.2020.9095554(532-536)Online publication date: Apr-2020
https://doi.org/10.1109/ICCCBDA49378.2020.9095554
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Index Terms

Linear and Non-Linear Models for Purchase Prediction
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Information systems
  1. Information systems applications

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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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Publication History

Published: 16 September 2015

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Short-paper
Research
Refereed limited

Conference

RecSys '15

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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

View all

Zhang WWang M(2021)An improved deep forest model for prediction of e-commerce consumers’ repurchase behaviorPLOS ONE10.1371/journal.pone.025590616:9(e0255906)Online publication date: 20-Sep-2021
https://doi.org/10.1371/journal.pone.0255906
Zhu BTang WMao XYang W(2020)Location-based Hybrid Deep Learning Model for Purchase Prediction2020 5th International Conference on Computational Intelligence and Applications (ICCIA)10.1109/ICCIA49625.2020.00038(161-165)Online publication date: Jun-2020
https://doi.org/10.1109/ICCIA49625.2020.00038
Dou X(2020)Online Purchase Behavior Prediction and Analysis Using Ensemble Learning2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA49378.2020.9095554(532-536)Online publication date: Apr-2020
https://doi.org/10.1109/ICCCBDA49378.2020.9095554
Yang FHan XLang JLu WLiu LZhang LPan J(2018)Commodity Recommendation for Users Based on E-commerce DataProceedings of the 2nd International Conference on Big Data Research10.1145/3291801.3291803(146-149)Online publication date: 27-Oct-2018
https://dl.acm.org/doi/10.1145/3291801.3291803

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