skip to main content
10.1145/3209978.3210138acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper

Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks

Published: 27 June 2018 Publication History

Abstract

Rapid expansion of mobile devices has brought an unprecedented opportunity for mobile operators and content publishers to reach many users at any point in time. Understanding usage patterns of mobile applications (apps) is an integral task that precedes advertising efforts of providing relevant recommendations to users. However, this task can be very arduous due to the unstructured nature of app data, with sparseness in available information. This study proposes a novel approach to learn representations of mobile user actions using Deep Memory Networks. We validate the proposed approach on millions of app usage sessions built from large scale feeds of mobile app events and mobile purchase receipts. The empirical study demonstrates that the proposed approach performed better compared to several competitive baselines in terms of recommendation precision quality. To the best of our knowledge this is the first study analyzing app usage patterns for purchase recommendation.

References

[1]
Narayan Bhamidipati, Ravi Kant, and Shaunak Mishra . 2017. A large scale prediction engine for app install clicks and conversions Proc. of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 167--175.
[2]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean . 2013. Distributed representations of words and phrases and their compositionality Advances in Neural Information Processing Systems 26.
[3]
V. Radosavljevic, M. Grbovic, N. Djuric, N. Bhamidipati, D. Zhang, J. Wang, J. Dang, H. Huang, A. Nagarajan, and P. Chen . 2016. Smartphone app categorization for interest targeting in advertising marketplace. In International World Wide Web Conference (WWW).
[4]
Jean Sébastien, Cho KyungHyun, Roland Memisevic, and Yoshua Bengio . 2015. On using very large target vocabulary for neural machine translation Proc. of ACL. 1--10.
[5]
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et almbox. . 2015. End-to-end memory networks. In Advances in neural information processing systems. 2440--2448.
[6]
Ilya Sutskever, Oriol Vinyals, and Quoc V Le . 2014. Sequence to sequence learning with neural networks Advances in neural information processing systems. 3104--3112.
[7]
Vincent Wenchen Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang . 2010. Collaborative filtering meets mobile recommendation: A user-centered approach. In AAAI, Vol. Vol. 10. 236--241.
[8]
Hengshu Zhu, Enhong Chen, Hui Xiong, Kuifei Yu, Huanhuan Cao, and Jilei Tian . 2015. Mining mobile user preferences for personalized context-aware recommendation. ACM Trans. Intell. Syst. Technol. (TIST) Vol. 5, 4 (2015), 58.

Cited By

View all
  • (2024)Customer purchase prediction in B2C e-business: A systematic review and future research agendaExpert Systems with Applications10.1016/j.eswa.2024.124261252(124261)Online publication date: Oct-2024
  • (2023)An Improved Recommendation Algorithm Based on Attention Mechanism2023 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)10.1109/AEECA59734.2023.00118(632-637)Online publication date: 18-Aug-2023
  • (2022)A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3145690(1-1)Online publication date: 2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
June 2018
1509 pages
ISBN:9781450356572
DOI:10.1145/3209978
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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. deep memory networks
  2. mobile advertising

Qualifiers

  • Short-paper

Conference

SIGIR '18
Sponsor:

Acceptance Rates

SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)10
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Customer purchase prediction in B2C e-business: A systematic review and future research agendaExpert Systems with Applications10.1016/j.eswa.2024.124261252(124261)Online publication date: Oct-2024
  • (2023)An Improved Recommendation Algorithm Based on Attention Mechanism2023 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)10.1109/AEECA59734.2023.00118(632-637)Online publication date: 18-Aug-2023
  • (2022)A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3145690(1-1)Online publication date: 2022
  • (2022)Predicting Actions of Users Using Heterogeneous Online SignalsBig Data10.1089/big.2021.032010:4(298-312)Online publication date: 1-Aug-2022
  • (2021)Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design FrameworkInformation10.3390/info1211048012:11(480)Online publication date: 19-Nov-2021
  • (2021)Tolerance-Oriented Wi-Fi Advertisement Scheduling: A Near Optimal Study on Accumulative User InterestsMobile Networks and Applications10.1007/s11036-021-01849-8Online publication date: 8-Dec-2021
  • (2020)General-Purpose User Embeddings based on Mobile App UsageProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403334(2831-2840)Online publication date: 23-Aug-2020
  • (2020)Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware ModelProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412739(2461-2468)Online publication date: 19-Oct-2020
  • (2020)A Neighbor-Guided Memory-Based Neural Network for Session-Aware RecommendationIEEE Access10.1109/ACCESS.2020.30063608(120668-120678)Online publication date: 2020
  • (2019)A Collaborative Session-based Recommendation Approach with Parallel Memory ModulesProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331210(345-354)Online publication date: 18-Jul-2019
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media