Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mobile advertisement distribution effects are vitally important for advertisers as ll as users. Status quo studies are lacking of efficient distribution especially when user traces and budgets are involved. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location-centric mobile crowdsourcing network instead of conventional user-centric and platform, where locations are vitally important for advertisement distribution. To this end, this work focuses on the mobile advertisement user selection problem when interested area coverage (IAC) is considered. Unfortunately, developing location-centric needs to deal with the spatio-temporal features in each user, and IAC coverage needs to be effectively counted. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes following efforts: First, a budget-constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved to be 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%.
['Wanru Xu', 'Panlong Yang', 'Maotian Zhang', 'Chaocan Xiang', 'Yiwei Xu', 'Ping Li', 'Xuangou Wu']
I am writing a research paper on a idea Near Optimal Mobile Advertisement User Selection with Interested Area Coverage
[{"from": "human", "value": "I am writing a research paper on a idea Near Optimal Mobile Advertisement User Selection with Interested Area Coverage"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mobile advertisement distribution effects are vitally important for advertisers as ll as users. Status quo studies are lacking of efficient distribution especially when user traces and budgets are involved. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location-centric mobile crowdsourcing network instead of conventional user-centric and platform, where locations are vitally important for advertisement distribution. To this end, this work focuses on the mobile advertisement user selection problem when interested area coverage (IAC) is considered. Unfortunately, developing location-centric needs to deal with the spatio-temporal features in each user, and IAC coverage needs to be effectively counted. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes following efforts: First, a budget-constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved to be NP-hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio (1\u20131/e). Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%."}]