A Probe Into Users Interaction Optimization Of Knowledge Discovery System Based On Mental Model

We have moved from the digital age to the data-driven era,where data is both an asset and a resource.Faced with exponential growth and abundant data resources,the original information service system can not meet the needs of users' precise needs.How to discover the knowledge required by users and improve resource utilization has become the research focus of knowledge service innovation.The knowledge discovery system has aroused warm response once put forward,which provides new ideas for knowledge innovation and service.Big data research and technology application has promoted the knowledge discovery service from the theoretical level to the practical application.Based on the knowledge discovery system,the knowledge discovery service uses the latest network tools?theories? technologies etc,integrates the concepts of association and collaboration,and provides intelligent knowledge services for users to observe information,find resources,discover knowledge,and provide intelligent knowledge services.It satisfies the needs of users,promotes the efficient realization of knowledge transfer and utilization,knowledge dissemination and sharing,knowledge absorption and creation.Driven by data,the knowledge discovery system takes resources as the foundation,technology as the premise and service as the window to meet users' knowledge needs.Driven by users' demands,the resource is related to the channel of interaction between knowledge discovery system and users,technology determines the depth of resource discovery,and service reflects the quality of knowledge discovery service.The interaction between users and knowledge discovery system is mainly reflected in resource layer,technology layer and service layer.Therefore,it is necessary to develop the interaction function of knowledge discovery system from the perspective of interaction,to meet the needs of users,enhance users' interaction experience,and promote the multiplication of knowledge value.This paper focuses on the interaction among users and resources,technology,services,summarizes the interactive balance implementation mechanism,and explores users' cognition and emotions.From an empirical perspective,we measure the mentalmodel of users' interaction with the superstar discovery system,explore the users' perception and evaluation of the superstar discovery system,and find out the aspects that the current discovery system does not match the users' needs.According to the experimental results,we build the optimization paths and propose corresponding optimization strategies to improve the service quality of the knowledge discovery system,so that the system can better provide users with knowledge retrieval,knowledge recommendation,knowledge consulting,domain knowledge analysis,knowledge value re-creation and other services.This paper takes interaction optimization as the goal,takes user cognition theory,emotion cognition theory,service interaction theory as the theoretical basis,and carries out the research from the following aspects:Firstly,this paper analyzes the interaction between knowledge discovery system and user' mental model,takes the relationship between interaction and mental model as the starting point,expounds the influencing factors of mental model,describes the interaction dimension,we combine the balance mechanism of “assimilation” and“compliance” in the user's mental model with the interaction balance between users and knowledge discovery systems,propose the interaction balance mechanism.Secondly,we take the superstar discovery system as an example to carry out experiments.Through interview,we observe the user's mental model in interaction,use cognitive concept coding and emotional concept coding methods to explore the user's current cognition and emotion for superstar discovery system,and find out the aspects that the current discovery system does not match the users' needs.Thirdly,according to the previous research,we put forward optimization strategies,the main performance is to optimize resources and services by users' demand and technology.Finally,this paper summarizes the research.In view of the content of the whole article,we put forward the limitations of this study,and according to the shortcomings of the study,we put forward the future research directions and prospects.