An Interest Feature Spatial Approach for Personalized Recommendation

Abstract

To expand user's actions of personalized recommendation, this paper introduces an Interest Feature Spatial based Recommendation Model. This model combines both collection behavior data of network users and content data of web pages located by URL address. The main content includes: (1) Proposing the construction of interest feature spatial based on SHG-Tree; (2) Proposing the formula to calculate interest feature values of network resources; (3) Proposing four interest match algorithms along with six types of personalized recommendation schemes. Experiments show that the recommendation service can achieve millisecond responding, the precision, especially recall metric is better than item-based collaborative filtering algorithm.