AI helps you reading Science

AI generates interpretation videos

AI extracts and analyses the key points of the paper to generate videos automatically


pub
Go Generating

AI Traceability

AI parses the academic lineage of this thesis


Master Reading Tree
Generate MRT

AI Insight

AI extracts a summary of this paper


Weibo:
Based on this influence model, we studied the Impression Counts for Outdoor Advertising problem and proved that it is NP-hard to approximate

Optimizing Impression Counts for Outdoor Advertising

pp.1205-1215 (2019)

Cited by: 14|Views372
EI

Abstract

In this paper we propose and study the problem of optimizing the influence of outdoor advertising (ad) when impression counts are taken into consideration. Given a database U of billboards, each of which has a location and a non-uniform cost, a trajectory database T and a budget B, it aims to find a set of billboards that has the maximum ...More

Code:

Data:

0
Introduction
  • Outdoor advertising has been a market of 29 billion dollars since 2017 and its revenue is expected to grow by 3% to 4% per year to reach 33 billion dollars by 20211. 74% of its growth comes from the billboard segment [1].
  • The evidence in the experimental study shows that (Figure 4a), more than 50% travellers are impressed by more than five billboards on each trip.
  • The aforementioned opportunities motivate them to propose and study a novel research problem, namely optimizing Impression Counts for Outdoor Advertising (ICOA).
  • Given a billboard database U, a trajectory database T and a budget B, ICOA aims to find a set of billboards that have the maximum influence under the budget.
  • That means the authors can save about $70,000/month if the authors can improve the influence by 10%
Highlights
  • Outdoor advertising has been a market of 29 billion dollars since 2017 and its revenue is expected to grow by 3% to 4% per year to reach 33 billion dollars by 20211. 74% of its growth comes from the billboard segment [1]
  • Given a billboard database U, a trajectory database T and a budget B, Impression Counts for Outdoor Advertising (ICOA) aims to find a set of billboards that have the maximum influence under the budget
  • In order to address this algorithmic challenge, we propose an upper bound estimation method that tightly upper bounds the logistic function value, by means of a tangent line that intersects with the logistic S-curve
  • We propose and study the ICOA problem for the first time, and show that the influence model based on the logistic function is non-submodular
  • We find more than 50% trajectories can pass over more than 5 billboards, which validates the motivation of this work as well as our use of the logistic function for influence modelling
  • Based on this influence model, we studied the ICOA problem and proved that it is NP-hard to approximate
Results
  • More than 80% drivers notice billboards when driving2. The evidence in the experimental study shows that (Figure 4a), more than 50% travellers are impressed by more than five billboards on each trip.
  • That means the authors can save about $70,000/month if the authors can improve the influence by 10%.
  • The authors find more than 50% trajectories can pass over more than 5 billboards, which validates the motivation of this work as well as the use of the logistic function for influence modelling.
  • The effectiveness of BBS and PBBS consistently outperform that of Greedy and Top-k by up to 60% and 300%, respectively.
  • LazyProbe has the best effectiveness , which outperforms BBS, Greedy and PBBS by up to 3%, 3% and 6% respectively
Conclusion
  • The authors first introduced a non-submodular influence model, which is widely adopted in many areas such as consumer behaviour and advertising marketing, etc.
  • Based on this influence model, the authors studied the ICOA problem and proved that it is NP-hard to approximate.
  • The authors conducted experiments on real-world datasets to verify the efficiency, effectiveness adaptability, and scalability of the methods
Tables
  • Table1: Related work
  • Table2: Frequently used notations
  • Table3: Statistics of datasets
  • Table4: Parameter settings
Download tables as Excel
Related work
  • In the following, we discuss the most relevant literature to this paper: Trajectory-driven Influential Billboard placement (TIP), Site Selection, and Location-aware IM (LIM). The main differences between existing works and ICOA are summarized in Table 1.

    TIP [32] is closely related to our problem, which also studies billboard placement to achieve the best advertising outcome. The core difference lies in the influence model. In particular, TIP assumes that a user (i.e., trajectory) can be influenced so long as one billboard is close enough to the trajectory the user travels along. Under such an influence model, when multiple billboards are close to a trajectory, the marginal influence is reduced to capture the property of diminishing returns. Therefore, TIP focuses on identifying and reducing the overlap of the influence among different billboards to the same trajectories, while keeping the budget constraint into consideration. That is, TIP can maximize the number of distinct users by impressing as many people as possible for one time. It does not consider the relationship between the influence effect and counts of impressions on one user because the model assumes one time impression is enough. ICOA is built upon a logistic influence model which has been widely adopted in consumer behavior studies. To maximize the influence to users, we need to control the overlap to some extent by impressing the same users several times. Unfortunately, the logistic influence model is non-submodular. Adapting the greedy approach to ICOA, which effectively solves TIP, could lead to arbitrarily bad solutions due to the non-submodular of the influence function.
Funding
  • Zhifeng Bao was partially supported by ARC DP170102726, DP18010 2050, and NSFC 61728204, 91646204, and Google Faculty Award
  • This research was supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier I grant MSS18C001
Reference
  • Penneco Outdoor Advertising. 2016. Billboard Statistics. https://www.pennecooutdoor.com/billboard-statistics
    Findings
  • Sara Ahmadian, Zachary Friggstad, and Chaitanya Swamy. 2013. Local-Search based Approximation Algorithms for Mobile Facility Location Problems. In SODA. SIAM, 1607–1621.
    Google ScholarFindings
  • Christoph Ambühl, Monaldo Mastrolilli, and Ola Svensson. 2011. Inapproximability Results for Maximum Edge Biclique, Minimum Linear Arrangement, and Sparsest Cut. SIAM J. Comput. 40, 2 (2011), 567–596.
    Google ScholarLocate open access versionFindings
  • Margaret C Campbell and Kevin Lane Keller. 2003. Brand familiarity and advertising repetition effects. Journal of consumer research 30, 2 (2003), 292–304.
    Google ScholarLocate open access versionFindings
  • Can Chen, Junming Liu, Qiao Li, Yijun Wang, Hui Xiong, and Shanshan Wu. 2017. Warehouse Site Selection for Online Retailers in Inter-Connected Warehouse Networks. In ICDM. IEEE, 805–810.
    Google ScholarLocate open access versionFindings
  • Farhana Murtaza Choudhury, J. Shane Culpepper, Zhifeng Bao, and Timos Sellis. 2018. Finding the optimal location and keywords in obstructed and unobstructed space. VLDB J. 27, 4 (2018), 445–470.
    Google ScholarLocate open access versionFindings
  • Gershon Feder, Richard E Just, and David Zilberman. 1985. Adoption of agricultural innovations in developing countries: A survey. Economic development and cultural change 33, 2 (1985), 255–298.
    Google ScholarFindings
  • Gerald J Gorn and Marvin E Goldberg. 1980. Children’s responses to repetitive television commercials. Journal of Consumer Research 6, 4 (1980), 421–424.
    Google ScholarLocate open access versionFindings
  • William H Greene. 2003. Econometric analysis. Pearson Education India.
    Google ScholarFindings
  • Johny K Johansson. 1979. Advertising and the S-curve: A new approach. Journal of Marketing Research (1979), 346–354.
    Google ScholarLocate open access versionFindings
  • Samir Khuller, Anna Moss, and Joseph Naor. 1999. The Budgeted Maximum Coverage Problem. Inf. Process. Lett. 70, 1 (1999), 39–45.
    Google ScholarLocate open access versionFindings
  • LAMAR. 2017. National Rate Card. http://apps.lamar.com/demographicrates/
    Findings
  • Sang Yup Lee. 2014. Examining the factors that influence early adopters’ smartphone adoption: The case of college students. Telematics and Informatics 31, 2 (2014), 308–318.
    Google ScholarLocate open access versionFindings
  • Guoliang Li, Shuo Chen, Jianhua Feng, Kian-Lee Tan, and Wen-Syan Li. 20Efficient location-aware influence maximization. In SIGMOD. ACM, 87–98.
    Google ScholarLocate open access versionFindings
  • Shi Li. 2019. On Facility Location with General Lower Bounds. In SODA. SIAM, 2279–2290.
    Google ScholarFindings
  • John DC Little. 1979. Aggregate advertising models: The state of the art. Operations research 27, 4 (1979), 629–667.
    Google ScholarLocate open access versionFindings
  • Dongyu Liu, Di Weng, Yuhong Li, Jie Bao, Yu Zheng, Huamin Qu, and Yingcai Wu. 20SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations. IEEE Trans. Vis. Comput. Graph. 23, 1 (2017), 1–10.
    Google ScholarLocate open access versionFindings
  • Yubao Liu, Raymond Chi-Wing Wong, Ke Wang, Zhijie Li, Cheng Chen, and Zitong Chen. 2013. A new approach for maximizing bichromatic reverse nearest neighbor search. Knowl. Inf. Syst. 36, 1 (2013), 23–58.
    Google ScholarLocate open access versionFindings
  • Prashant Malaviya. 2007. The moderating influence of advertising context on ad repetition effects: The role of amount and type of elaboration. Journal of Consumer Research 34, 1 (2007), 32–40.
    Google ScholarLocate open access versionFindings
  • M. Teresa Melo, Stefan Nickel, and Francisco Saldanha-da-Gama. 2006. Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Computers & OR 33 (2006), 181–208.
    Google ScholarLocate open access versionFindings
  • M. Teresa Melo, Stefan Nickel, and Francisco Saldanha-da-Gama. 2009. Facility location and supply chain management - A review. European Journal of Operational Research 196, 2 (2009), 401–412.
    Google ScholarLocate open access versionFindings
  • George A Miller. 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological review 63, 2 (1956), 81.
    Google ScholarLocate open access versionFindings
  • Kristian S Palda. 1965. The measurement of cumulative advertising effects. The Journal of Business 38, 2 (1965), 162–179.
    Google ScholarLocate open access versionFindings
  • William Sierzchula, Sjoerd Bakker, Kees Maat, and Bert Van Wee. 2014. The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy 68 (2014), 183–194.
    Google ScholarLocate open access versionFindings
  • Julian L Simon and Johan Arndt. 1980. The shape of the advertising response function. Journal of Advertising Research (1980).
    Google ScholarLocate open access versionFindings
  • Jennifer Taylor, Rachel Kennedy, and Byron Sharp. 2009. Is once really enough? Making generalizations about advertising’s convex sales response function. Journal of Advertising Research 49, 2 (2009), 198–200.
    Google ScholarLocate open access versionFindings
  • Gerard J Tellis. 1988. Advertising exposure, loyalty, and brand purchase: A two-stage model of choice. Journal of marketing research (1988), 134–144.
    Google ScholarLocate open access versionFindings
  • Kenneth E Train. 2009. Discrete choice methods with simulation. Cambridge university press.
    Google ScholarFindings
  • Demetrios Vakratsas, Fred M Feinberg, Frank M Bass, and Gurumurthy Kalyanaram. 2004. The shape of advertising response functions revisited: A model of dynamic probabilistic thresholds. Marketing Science 23, 1 (2004), 109–119.
    Google ScholarLocate open access versionFindings
  • Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Timos Sellis, and Gao Cong. 2018. Reverse k Nearest Neighbor Search over Trajectories. IEEE Trans. Knowl. Data Eng. 30, 4 (2018), 757–771.
    Google ScholarLocate open access versionFindings
  • Raymond Chi-Wing Wong, M. Tamer Özsu, Philip S. Yu, Ada Wai-Chee Fu, and Lian Liu. 2009. Efficient Method for Maximizing Bichromatic Reverse Nearest Neighbor. PVLDB 2, 1 (2009), 1126–1137.
    Google ScholarLocate open access versionFindings
  • Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, and Zhiyong Peng. 2018. Trajectory-driven Influential Billboard Placement. In SIGKDD. ACM, 2748–2757.
    Google ScholarLocate open access versionFindings
  • Zenan Zhou, Wei Wu, Xiaohui Li, Mong-Li Lee, and Wynne Hsu. 2011. MaxFirst for MaxBRkNN. In ICDE. IEEE, 828–839.
    Google ScholarLocate open access versionFindings
Your rating :
0

 

Tags
Comments
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn
小科