Outdoor Online Advertising Model Based On Urban Multi-source Data

As a traditional advertising medium,the overall scale of my country's consumer outdoor media market shows a trend of sustained and rapid growth.However,due to the lack of effective data information for advertising audiences,on the one hand,outdoor media advertising cannot effectively solve the problem of precise delivery;on the other hand,there is a lack of accurate quantitative calculations for the propagation effect(influence)of advertising.The big data of the "digital era" provides the possibility to solve the above problems-1)excavate the user's travel intentions from a large number of urban multi-source data,and use the matching of the advertising theme and travel intentions to achieve accurate advertising to a certain extent;2)By analyzing the user's spatial and temporal mobility,the influence of offline advertising in the physical world can be constructed.Based on this,this paper has carried out research on the recommendation of outdoor advertising placement based on urban multi-source data,and recommended the best placement of outdoor advertising with the goal of precise placement and maximizing the influence of communication.The research work of this paper mainly includes:(1)Outdoor advertising marketing is an offline promotion of advertising information,which cannot quickly and accurately quantify its influence and publicity effect.This article fuses urban taxi trajectory data,road network data,and POI data,etc.,by describing the travel intentions and advertising exposure intensity of group users,an outdoor advertising influence model for potential target users is proposed;at the same time,based on urban commercial facilities and public transportation The distribution of facilities analyzes the flow of people on different roads,and uses this to design a pricing mechanism for outdoor advertising to be placed in different spatial areas.(2)Considering the conflict between advertising influence and delivery cost,based on the outdoor advertising influence model and pricing mechanism,the Skyline query algorithm is used to formally define the goal of advertising influence-delivery cost Recommended placement of precise ads.Due to the large number of result sets of the traditional G-Sky line query algorithm and the low efficiency when dealing with the location recommendation problem,this paper proposes an improved G-Skyline query algorithm based on the calculation of the maximum dominating number and an efficient pruning strategy.Through experimental simulation tests,improved G-Skyline query has greatly improved the efficiency of the algorithm,and has better performance in both advertising influence and delivery cost.(3)Analyze the "overlapping effect" of outdoor advertising influence due to user movement,and rebuild the outdoor advertising communication influence model based on user migration.This defines the problem of recommending the location of k ad serving units within the search space-time scope with the goal of maximizing the influence of ad communication.In order to solve this combinatorial optimization problem,this paper uses a divide and conquer mechanism and proposes an optimal search method based on utility evaluation.Experiments prove that the algorithm has a significant improvement in operating efficiency under the premise of a small loss of expected influence compared to the benchmark method.