Optimizing the interested area coverage with efficient mobile advertisement user selection. (28th April 2017)

• Record Type: Journal Article
• Authors: Xu, Wanru; Yang, Panlong
• Other Names: Bu Kai guestEditor.; Xiao Bin guestEditor.; Qian Yi guestEditor.; Dong Fang guestEditor.; Shen Jun guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 19(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-04-28
• Subjects: interest area coverage -- mobile advertisement -- user selection; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4159
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Mobile advertisement distribution effects are vitally important for advertisers as well as users. Status quo studies are focusing on efficient distribution especially when user mobilities are involved. Unfortunately, previous studies have shown the interested area property during mobile advertisement propagation. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location‐centric mobile crowdsourcing network, where locations are vitally important for advertisement distribution, and mobile users need to be carefully selected for efficiency considerations. Different from traditional user‐centric and platform‐centric crowdsourcing networks, this work focuses on the mobile advertisement user selection problem when interested area coverage is considered. There are several fundamentally important challenges needed to be addressed before developing a location‐centric scheme for mobile advertisement user selection. First of all, we need to deal with the spatio‐temporal features for each user, where the interested area coverage ratio needs to be effectively evaluated. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes the following efforts: First, a budget‐constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved NP‐hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio ( 1 − 1 e ) . Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%. When considering the user's interest to different advertisements, the real user interest data set has also been used to validate that our proposed method could achieve improved performance than the random method.