Users Modeling Of We-Maps In Multi-dimensional Scenarios

" We-Map " is a product of emerging maps in the self-media era.It is a map for the general public,with simple production and easy dissemination as its core.In order to better use the functions of We-Map,it is necessary to study We-Map users and their behaviors while improving the theory of maps in the self-media era,exploring methods for rapid mapping,and solving the current situation of high standard requirements to cartographers.In addition,in the production process of the We-Map software platform,a suitable user model not only has a clear guiding role in the development and operation of the software,but also plays a vital role in the user's personalized service in the later stage.In this paper,we will combine the characteristics of the " little,flexible and fast " of the We-Map and the characteristics of the user of the We-Map as "both a cartographer and a viewer",and use the method of multi-dimensional situation analysis to establish the primary user model,and then mine the user behavior mechanism based on the communication capacity index,and establish the high-level model of the We-Map user.Specifically,firstly,analyze the user's attribute information and behavior information in a multi-dimensional scenario to make it accurately described;and then combine the "point-to-point" dissemination method in the self-media era to conduct association rule mining on user behavior,infer user needs,preferences and behaviors;and finally recommend easy-to-make and easy-to-spread map symbols or templates for users,to makes the We-Map convenient and fast for communication.Based on this,the research work and main results of this article include:(1)Proposed a multi-dimensional scenario analysis method for we-map users.Starting from the "little,flexible and fast" characteristics of We-Map,a multi-dimensional scenario analysis method suitable for We-Map users is proposed,which expresses user information and preferences from a variety of scenario dimensions,standardizes user information and preferences,and establishes a primary user model.(2)Designed a We-Map user association rule mining algorithm under the constraint of communication capacity.Starting from the dissemination effect of the We-Map,search for user behaviors that affect the dissemination effect of the we-map,and mine the hidden association rules.For the mining results,filter useless association rules,feedback and update useful rules,and optimize the primary user model.(3)Established a We-Map user model.Based on the Bayesian modeling method,the optimized primary user model uses the method of rule clustering to generate the advanced user model,which includes the individual user model and the group user model.Finally,through the analysis of related algorithms,it is possible to recommend high-dissemination We-Map templates and symbols for users to assist users in drawing.In general,this article combines the analysis methods of multi-dimensional scenarios,and uses the communication capacity index to constrain the user behavior association rules,and finally establishes a We-Map user model.The user model can manage and analyze We-Map users attributes and behavior information,and recommend easy-to-spread map symbols and templates in the process of user mapping,so that users can make We-Map more quickly.