Research And Application Of Resource Recommendation Based On Pragmatics Context

With the rapid advancement of the Internet, the information overload has been a primal problem of Internet users. Resource recommendation system provides a very effective means of solving this problem. It can provide users with information filtering and the service of resource recommendation to improve their work efficiency, which is gradually welcomed by most users. So the research of resource recommendation is becoming a vital area. Presently, the main resource recommendation systems are system based on rules, one on contents and collaborative filtering recommendation.System based on rules is formed from user and rule model. It can meet users'real-time needs, but the formulation of rules requires the involvement of experts, and the efficiency will be lowered, for the emergence of deviation and problems that is difficult to update. System based on contents chooses what is going to be recommended according to the contents of the information and the user interest. The shortcoming is that it relays only on user interest, and cannot find where the new interest of user. Therefore, its recommendation is restricted in the area of history list.Among current resource recommendation systems, the collaborative filtering recommendation is the most successful one and extensively used in a number of resource recommendation systems. Collaborative filtering recommendation starts from user to seek the nearest neighbor user of the object user. By evaluating the other resource's weighted evaluation value of the nearest neighbor as the target user's expectation, it provided the resources to user, So the potential interest of user will be found. Though with a good many advantages, collaborative filtering recommendation has been successfully applied, the traditional collaborative filtering recommendation based on the project grade calculates similar neighbors of the object user then recommends resource. This will cause data sparsity and cold start, and the interest of users can not be fully reflected by the grade.To settle problem above, the paper proposes a recommendation system based on pragmatics. On the basis of the traditional collaborative filtering recommendation, introduce the situation factor and factors of trust of pragmatics into the research of resource recommendation. Among all these information, the contextual information and scene information that influence user's behaviors turn to user scenarios, and the user scenarios that militates user decision turns to the most prominent scenarios. This method acquires user's prominent scenarios in the beginning, combines them to calculates user interest relativity, rating similarity and rating correlation matrix, then forms the correlation model of user interest, finally, calculates user confidence grade. Taking the user confidence grade as user score weight, combine the traditional method to predict the grade of object user.This paper adopts Matlab and dose an simulation experiment in MovieLens. The experiment data shows that the method has a certain feasibility and effectiveness, and compared with the traditional one, the Slope One algorithm can reduce absolute average error. And it provides a new direction for current resource recommendation technology. Finally, combining with the intelligent educational platform project of Higher Education Press based on ontology, semantic and pragmatic, the paper designs a resource recommendation model based on user learning situation to explain the practical application of this method.