Open Access. Powered by Scholars. Published by Universities.®
Advertising and Promotion Management
Research Collection School Of Computing and Information Systems
- Keyword
-
- Influence Maximization (2)
- Outdoor Advertising (2)
- Ad-supported business models (1)
- Advertisement (1)
- Context predictions (1)
-
- Context uncertainty (1)
- Data mining (1)
- Design and implementations (1)
- E-commerce (1)
- Economic analysis (1)
- Ensemble learning (1)
- Event processing (1)
- Face detection (1)
- Feature engineering (1)
- Fraud detection (1)
- Game theory (1)
- Gender diversity (1)
- Imbalanced classification (1)
- Logistic Function (1)
- Mobile (1)
- Mobile advertisement (1)
- Mobile advertising (1)
- Moving Trajectory (1)
- Negotiation protocol (1)
- Non-submodularity (1)
- Online advertising (1)
- Proactive retrieval (1)
- Racial diversity (1)
- Resource consumption (1)
- Trajectory (1)
Articles 1 - 8 of 8
Full-Text Articles in Business
Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak
Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Gender and racial diversity in the mediated images from the media shape our perception of different demographic groups. In this work, we investigate gender and racial diversity of 85,957 advertising images shared by the 73 top international brands on Instagram and Facebook. We hope that our analyses give guidelines on how to build a fully automated watchdog for gender and racial diversity in online advertisements.
Optimizing Impression Counts For Outdoor Advertising, Yipeng Zhang, Yuchen Li, Zhifeng Bao, Songsong Mo, Ping Zhang
Optimizing Impression Counts For Outdoor Advertising, Yipeng Zhang, Yuchen Li, Zhifeng Bao, Songsong Mo, Ping Zhang
Research Collection School Of Computing and Information Systems
In this paper we propose and study the problem of optimizing theinfluence of outdoor advertising (ad) when impression counts aretaken into consideration. Given a database U of billboards, each ofwhich has a location and a non-uniform cost, a trajectory databaseT and a budget B, it aims to find a set of billboards that has themaximum influence under the budget. In line with the advertisingconsumer behavior studies, we adopt the logistic function to takeinto account the impression counts of an ad (placed at differentbillboards) to a user trajectory when defining the influence measurement. However, this poses two challenges: (1) our problemis …
Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Research Collection School Of Computing and Information Systems
In this paper we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T and a budget L, find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1−1/e) approximation ratio. However, the enumeration should be …
Real-Time Targeted Influence Maximization For Online Advertisements, Yuchen Li, Dongxiang Zhang, Kian-Lee Tan
Real-Time Targeted Influence Maximization For Online Advertisements, Yuchen Li, Dongxiang Zhang, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Advertising in social network has become a multi-billion dollar industry. A main challenge is to identify key influencers who can effectively contribute to the dissemination of information. Although the influence maximization problem, which finds a seed set of k most influential users based on certain propagation models, has been well studied, it is not target-aware and cannot be directly applied to online advertising. In this paper, we propose a new problem, named Keyword-Based Targeted Influence Maximization (KB-TIM), to find a seed set that maximizes the expected influence over users who are relevant to a given advertisement. To solve the problem, …
Handling Location Uncertainty In Event Driven Experimentation, Kartik Muralidharan, Srinivasan Seshan, Narayan Ramasubbu, Rajesh Krishna Balan
Handling Location Uncertainty In Event Driven Experimentation, Kartik Muralidharan, Srinivasan Seshan, Narayan Ramasubbu, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
The wide spread use of smart phones has ushered in a wave of context-based advertising services that operate on pre-defined user events. A prime example is Location Based Advertising. What is missing though, is the ability to experiment with these services under varying event conditions with real users using their regular phones in real-world environments. Such experiments provide greater insight into user needs for and responsiveness towards context-based advertising applications. However, these event-driven experiments rely on data that arrive from sources such as mobile sensors which have inherent uncertainties associated with them. This effects the interpretation of the outcome of …
Detecting Click Fraud In Online Advertising: A Data Mining Approach, Richard Oentaryo, Ee Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar
Detecting Click Fraud In Online Advertising: A Data Mining Approach, Richard Oentaryo, Ee Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar
Research Collection School Of Computing and Information Systems
Click fraud - the deliberate clicking on advertisements with no real interest on the product or service offered - is one of the most daunting problems in online advertising. Building an elective fraud detection method is thus pivotal for online advertising businesses. We organized a Fraud Detection in Mobile Advertising (FDMA) 2012 Competition, opening the opportunity for participants to work on real-world fraud data from BuzzCity Pte. Ltd., a global mobile advertising company based in Singapore. In particular, the task is to identify fraudulent publishers who generate illegitimate clicks, and distinguish them from normal publishers. The competition was held from …
Cameo: A Middleware For Mobile Advertisement Delivery, Azeem J. Khan, Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Krishna Balan, Srinivasan Seshan
Cameo: A Middleware For Mobile Advertisement Delivery, Azeem J. Khan, Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Krishna Balan, Srinivasan Seshan
Research Collection School Of Computing and Information Systems
Advertisements are the de-facto currency of the Internet with many popular applications (e.g. Angry Birds) and online services (e.g., YouTube) relying on advertisement generated revenue. However, the current economic models and mechanisms for mobile advertising are fundamentally not sustainable and far from ideal. In particular, as we show, applications which use mobile advertising are capable of using significant amounts of a mobile users' critical resources without being controlled or held accountable. This paper seeks to redress this situation by enabling advertisement supported applications to become significantly more "user-friendly". To this end, we present the design and implementation of CAMEO, a …
Vertical Differentiation And A Comparison Of Online Advertising Models, Mei Lin, Xuqing Ke, Andrew B. Whinston
Vertical Differentiation And A Comparison Of Online Advertising Models, Mei Lin, Xuqing Ke, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
Designing business models that take into consideration the role of advertising support is critical to the success of online services. In this paper, we address the challenges of these business model strategies and compare different ad revenue models. We use game theory to model vertical differentiation in both monopoly and duopoly settings, in which online service providers may offer an ad-free service, an ad-supported service, or a combination of these services. Offering both ad-free and ad-supported services is the optimal strategy for a monopolist because ad revenues compensate for the cannibalistic effect of vertical differentiation. In a duopoly equilibrium, exactly …