research-article

Data-driven Targeted Advertising Recommendation System for Outdoor Billboard

Authors:

Fei XiongAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 2

Article No.: 29, Pages 1 - 23

https://doi.org/10.1145/3495159

Published: 05 January 2022 Publication History

Abstract

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches.

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Index Terms

Data-driven Targeted Advertising Recommendation System for Outdoor Billboard
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 2

April 2022

392 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3508464

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

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: 05 January 2022

Accepted: 01 October 2021

Revised: 01 July 2021

Received: 01 December 2020

Published in�TIST�Volume 13, Issue 2

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Funding Sources

National Key Research and Development Program of China
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Beijing Nova Program
Beijing Municipal Science and Technology Commission, University of Macau
FDCT Macau SAR

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Cited By

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https://dl.acm.org/doi/10.1145/3649892
Hao MCai MFang MYou L(2024)SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation SystemsACM Transactions on Intelligent Systems and Technology10.1145/364386115:2(1-20)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3643861
Zou TYu LSun LDu BWang DZhuang F(2024)Event-Based Dynamic Graph Representation Learning for Patent Application Trend PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331233336:5(1951-1963)Online publication date: May-2024
https://doi.org/10.1109/TKDE.2023.3312333
Nguyen TNguyen TNguyen TYin HNguyen TJo JNguyen Q(2023)Isomorphic Graph Embedding for Progressive Maximal Frequent Subgraph MiningACM Transactions on Intelligent Systems and Technology10.1145/363063515:1(1-26)Online publication date: 19-Dec-2023
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Wu XZhang DZhang MGuo CYang BJensen C(2023)AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series ForecastingProceedings of the ACM on Management of Data10.1145/35889511:1(1-26)Online publication date: 30-May-2023
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Hu ZHuang DWang S(2023)Joint Optimization of Bus Scheduling and Targeted Bus Exterior AdvertisingJournal of Transportation Engineering, Part A: Systems10.1061/JTEPBS.TEENG-7812149:5Online publication date: May-2023
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Sun KQian TZhong MLi X(2023)Towards more effective encoders in pre-training for sequential recommendationWorld Wide Web10.1007/s11280-023-01163-126:5(2801-2832)Online publication date: 12-May-2023
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