A new ontology-based user modeling method for personalized recommendation

Personalized recommendation is an effective method to resolve the current problem of Internet information overload. In the recommendation systems, user modeling is a crucial step. Whether the model can accurately describe the users´ interests directly determines the quality of the personalized recommendations. At present in most personalized service systems keywords models or user-item models are used to describe the users´ preferences, but vectors or matrixes used in these models do not contain semantic information, so it is difficult to accurately model the users´ interests and hobbies, and it is also hard to extend the users´ interests. Ontology as a tool used to describe the domain knowledge is very powerful in conceptual describing and logical reasoning. Computation of the neighbor set of users or resources is also an important step in the recommendation, but at present three commonly used similarity algorithms have some shortcomings which lead the system sometimes difficulty to find similar users or resources. This paper presents a new ontology-based user modeling approach and an improved similarity algorithm. Our experiments show that the user model presented in this paper can effectively describe the users´ personalized preferences, and we also prove that the improved similarity algorithm is better than other three commonly used similarity algorithms.