Context Semantic Modeling And Reasoning On Personalized Customer Product Information Service

In terms of the booming digital economy,consumers still face the problem of"information overload" and "information trek".Personalized consumer product information service emerges,and gradually becomes the trend of precision marketing in the digital age.However,the development of personalized consumer product information service still has the problem of low matching degree,poor accuracy and unable to adapt to adjustment in different application scenarios.As a result,it is crucial to carry out systematic and rigorous academic research on the intelligent technology related to personalized consumer product information service.Through combing the literature at home and abroad,this paper found that the recommendation methods based on collaborative filtering and content are relatively commonly used when building recommendation system,and the theory foundation of recommendation method based on ontology is solid,and apparently superior to two recommendations mentioned before.And methods base on ontology are improvement and supplement of collaborative filtering and content-based recommendation method.In order to enrich the research on intelligent technology related to personalized consumer product information service,this paper utilize relevant theory including Theory of Ontology and Theory of Context Awareness,which are employed to build the commodity classification model of personalized commodity information service and context semantic model.Through the ontology building theory,this paper clearly defined classes,properties,and instance in the context semantic model.And using the method of Description Logic and rules engine,formulate a set of complete context reasoning procedure and come up with the implementation methods.We set up description logic reasoning layer and rule reasoning layer to model and reason context information,with which we can excavate latent semantic knowledge.With the aid of open sourced tools Jena and Pellet,this paper designed corresponding algorithm.The evaluation mechanism was set up for semantic modeling and evaluation of reasoning mechanism respectively,and the evaluation was made based on the method of SPAQL query and algorithm experiment.The conclusion of this paper shows that:Modeling product domain knowledge using ontology and using context semantic information within ontology model can effectively improve the reusability,context sensitivity and reliability of personalized product information service;In the personalized customer product information service,the inference of the context information can integrate and process the low-level situation information,and generates higher level semantics to satisfy the intelligent demand.By establishing the evaluation mechanism to evaluate the effect of the context semantic model,it can verify the reliability and consistency of the ontology model constructed from conceptual layer,semantic layer and reasoning layer.The effect of the context reasoning mechanism is evaluated by the performance experiment,which can verify the response time performance and the accuracy of the reasoning mechanism.The contribution of this paper is that:based on the theory of ontology and the theory of context awareness model,this paper builds a model of the personalized customer product information.The context semantics model is divided into customer product categories ontology model,direct situation ontology model and indirect situation ontology model,and illustrate the build process in detail;Combines the relevant theories of context reasoning and description logic,reasoning process and implementation steps have been clarified.And context reasoning model is divided into description logic reasoning layer and rule-based reasoning layer,in each layer the related rules are clearly defined as the basis for the subsequent inference implementation steps;Established the appraisal model of context semantic modeling and reasoning mechanism,design the corresponding evaluation index,and used the experimental results to illustrate the effects of context semantic modeling and reasoning mechanism.This paper provides personalized information service solution for related enterprises and provides new ideas for the academic exploration of personalized information service technology.