An study on personalized recommendation model based on search behaviors and resource properties

This paper presents an personalized recommendation model to recommend potentially interesting resources to users based on the users' search behaviors and resource properties. This model builds on the user-based collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: users' preference description, similar users' calculation and the resource recommending model. Firstly, our model generates users' preference to resources by calculating relevance score between query string and resource, the score of resource owner, the score of resource category and the score of browse sequence. Then it attains similar users by given user through calculated preferences before. Finally, it recommends filtered and sorted resources to users based top-N resource recommendation model. Our recommendation model is proved more accurate than the model purely based on users' search behaviors by the experiments of our paper.