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Recommendation for advertising messages on mobile devices

Authors:

Chih-Chun Chan,

Yu-Chieh Lin,

Ming-Syan ChenAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 235 - 236

https://doi.org/10.1145/2567948.2577343

Published: 07 April 2014 Publication History

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Abstract

Mobile devices, especially smart phones, have been popular in recent years. With users spending much time on mobile devices, service providers deliver advertising messages to mobile device users and look forward to increasing their revenue. However, delivery of proper advertising messages is challenging since strategies of advertising in TV, SMS, or website may not be applied to the banner-based advertising on mobile devices. In this work, we study how to properly recommend advertising messages for mobile device users. We propose a novel approach which simultaneously considers several important factors: user profile, apps used, and clicking history. We apply experiments on real-world mobile log data, and the results demonstrate the effectiveness of the proposed approach.

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Peng ZXu JChu XGao SYao YGu RTang Y(2022)VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain SystemsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30507819:1(173-186)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TNSE.2021.3050781
Viktoratos ITsadiras A(2021)Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design FrameworkInformation10.3390/info1211048012:11(480)Online publication date: 19-Nov-2021
https://doi.org/10.3390/info12110480
Wen DYang LLi HGuo KFei PLin H(2018)NLPCC 2017 Shared Task Social Media User Modeling Method Summary by DUTIR_923Natural Language Processing and Chinese Computing10.1007/978-3-319-73618-1_52(622-631)Online publication date: 5-Jan-2018
https://doi.org/10.1007/978-3-319-73618-1_52
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Index Terms

Recommendation for advertising messages on mobile devices
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, 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

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Poster

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WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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

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Cited By

View all

Peng ZXu JChu XGao SYao YGu RTang Y(2022)VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain SystemsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30507819:1(173-186)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TNSE.2021.3050781
Viktoratos ITsadiras A(2021)Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design FrameworkInformation10.3390/info1211048012:11(480)Online publication date: 19-Nov-2021
https://doi.org/10.3390/info12110480
Wen DYang LLi HGuo KFei PLin H(2018)NLPCC 2017 Shared Task Social Media User Modeling Method Summary by DUTIR_923Natural Language Processing and Chinese Computing10.1007/978-3-319-73618-1_52(622-631)Online publication date: 5-Jan-2018
https://doi.org/10.1007/978-3-319-73618-1_52
Yu CLiu YYao DYang LJin HChen HDing Q(2017)Modeling User Activity Patterns for Next-Place PredictionIEEE Systems Journal10.1109/JSYST.2015.244591911:2(1060-1071)Online publication date: Jun-2017
https://doi.org/10.1109/JSYST.2015.2445919

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Recommendations

The Influence of Mobile Self-Efficacy on Attitude towards Mobile Advertising

Modeling and Analyzing User Contexts for Mobile Advertising

User perception, intention, and attitude on mobile advertising