optimization method
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2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Liang Wang ◽  
Zhiwen Yu ◽  
Bin Guo ◽  
Dingqi Yang ◽  
Lianbo Ma ◽  

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.

2022 ◽  
Vol 73 ◽  
pp. 102245
Shintaro Iwamura ◽  
Yoshiki Mizukami ◽  
Takahiro Endo ◽  
Fumitoshi Matsuno

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