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Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site

Authors:

Nedim LipkaAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

Pages 133 - 134

https://doi.org/10.1145/2872518.2889394

Published: 11 April 2016 Publication History

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Abstract

This paper covers a sales forecasting problem on e-commerce sites. To predict product sales, we need to understand customers' browsing behavior and identify whether it is for purchase purpose or not. For this goal, we propose a new customer model, B2P, of aggregating predictive features extracted from customers' browsing history. We perform experiments on a real world e-commerce site and show that sales predictions by our model are consistently more accurate than those by existing state-of-the-art baselines.

References

[1]

S. Bhagat, A. Goyal, L. V.S. Lakshmanan. Maximizing product adoption in social networks. In WSDM, 2012.

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[2]

J.W. Byers, M. Mitzenmacher, G. Zervas. Daily Deals: Prediction, Social Diffusion, and Reputational Ramifications. In WSDM, 2012.

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Cited By

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Li CJiang WYang YPan SHuang GGuo L(2023)Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.315569034:11(9102-9115)Online publication date: Nov-2023
https://doi.org/10.1109/TNNLS.2022.3155690
Ariannezhad M(2022)Understanding and Learning from User Behavior for Recommendation in Multi-channel RetailAdvances in Information Retrieval10.1007/978-3-030-99739-7_56(455-462)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_56
Wan YChen YYan CZhang B(2021)Similarity-based sales forecasting using improved ConvLSTM and prophetIntelligent Data Analysis10.3233/IDA-20510325:2(383-396)Online publication date: 4-Mar-2021
https://doi.org/10.3233/IDA-205103
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Index Terms

Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics
  2. World Wide Web
    1. Web applications
      1. Electronic commerce
        E-commerce infrastructure
        Online shopping

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Information & Contributors

Information

Published In

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Poster

Funding Sources

The Institute for Information & Communications Technology Promotion (IITP) funded by the Korea government (MSIP)

Conference

WWW '16

Sponsor:

IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

Acceptance Rates

WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

View all

Li CJiang WYang YPan SHuang GGuo L(2023)Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.315569034:11(9102-9115)Online publication date: Nov-2023
https://doi.org/10.1109/TNNLS.2022.3155690
Ariannezhad M(2022)Understanding and Learning from User Behavior for Recommendation in Multi-channel RetailAdvances in Information Retrieval10.1007/978-3-030-99739-7_56(455-462)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_56
Wan YChen YYan CZhang B(2021)Similarity-based sales forecasting using improved ConvLSTM and prophetIntelligent Data Analysis10.3233/IDA-20510325:2(383-396)Online publication date: 4-Mar-2021
https://doi.org/10.3233/IDA-205103
Ariannezhad MJullien SNauts PFang MSchelter Sde Rijke MDemartini GZuccon GCulpepper JHuang ZTong H(2021)Understanding Multi-channel Customer Behavior in RetailProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482208(2867-2871)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482208
Khan MSaqib SAlyas TUr Rehman ASaeed YZeb AZareei MMohamed E(2020)Effective Demand Forecasting Model Using Business Intelligence Empowered With Machine LearningIEEE Access10.1109/ACCESS.2020.30037908(116013-116023)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3003790
CATAL CECE KArslan BAkbulut A(2019)Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales ForecastingBalkan Journal of Electrical and Computer Engineering10.17694/bajece.494920(20-26)Online publication date: 31-Jan-2019
https://doi.org/10.17694/bajece.494920
Tsai KWang YKuo HTsai JChang CHung HShuai H(2017)Multi-Source Learning for Sales Prediction2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI.2017.38(148-153)Online publication date: Dec-2017
https://doi.org/10.1109/TAAI.2017.38

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