Research On Key Technologies In Semantic Based Personalized Resource Recommendation System

With the advent of the information age and the development of Internet technology, it has become increasingly demanding that people focus on accessing peripheral information. However, the internet as an information encyclopedia not only has characteristics including open, heterogeneous, distributed and non-uniformity, but also updates and evolves extremely fast. In the disordered Internet, Information that users really interested in is rare. Faced with this flood of information, how to quickly and accurately meet user requirements to find the information has aroused the attention of scholars. Through investigation and study, researchers have proposed measures by building personalized recommendation system, to help users find the best resources based on the user’s preferences and requirements.Currently, there are a lot of technologies for building the recommendation system. However in actual application environments, the accuracy of the system has not been able to effectively improve. Main reasons are:1) Lacking of effective resource description model; 2) User preference model which is built based on current algorithms can not fully express the user’s preference while the user preference data collected by the system are usually not correct and sufficient; 3) Due to the huge amount of resources, the complexity of the algorithm can not be controlled well which will restrict the efficient of the system. This dissertation try to utilize semantic technology to solve above problems, the main contribution is described as follows:(1) Resource description model based on OntologyUse ontology with multi-hierarchical structure to build resource description model and extract relationship between resources; Extract semantic relation and background knowledge from ontology structure to reduce the user’s cognitive burden; By using those semantic information and algorithms proposed by this dissertation, the system can infer new information to complete the user preference model and make the preference elicitation process more simple and efficient. (2) Inference mechanism of the user preferenceBased on user preference description and abstracted semantic information from the resource description model, compute the semantic similarity between concepts and find the most close concept to enable the semantic propagation of the user preference between concepts in order to solve the problem of being not able to fully express the user’s preference as the user preference data collected by the system are usually not correct and sufficient.(3) Semantic similarity metric algorithmAccording to the preference inference process, based on extracted semantic information from ontology and common features of the concepts, compute the amount of propagated user preference to describe the degree of common features between concepts and express the similarity between concepts.(4) Ontology learning algorithm and personalization computationIn practical application environments, there exists no domain ontology. This dissertation proposes the tow ontology learning algorithms to automatically construct the ontology by utilize clustering algorithm and investigate the relation between user preference and resource. The first one allows system to build a set of distinct hierarchical ontologies, while the second extends classical agglomerative clustering and builds a multi-hierarchical ontology. An algorithm for selecting which ontology to use based on the user’s preferences is also presented to increase the accuracy of resource recommending.(5) Ontology based semantic resource recommendation modelBy integrating all algorithms and techniques brought, this dissertation proposes an ontology based semantic resource recommendation model to help users find the best resources based on the user’s preferences and requirements. Experiments are carried out to prove that our model performs better than traditional models in resource recommendation scenario.