Adaptive Updating Algorithm of User Model Based on Interest Cycle

User model is the base of supporting personalized recommendation. It is not only able to represent user´s interests, but also could reflect the change of user´s interests over time. However, existing user model updating approaches have several disadvantages, such as unified interest update mode, the same interest category without considering interest cycle, which reduce the accuracy of user model representing and updating. To solve these problems, this paper introduces a method to discover interest cycle by cutting valid time of interest existing in the curve of interest changing, and divides the interests into three kinds of categories which are long-term interest, medium-term interest and short-term interest. Based on the different kinds of interest, the disparate interest update strategies are presented to make change of interest adaptive, especially adaptive forgetting functions with the factor of interest forgotten and adaptive incenting functions with the factor of interest incented are constructed, both of them could reflect the actual intention of interest changing over time. It is shown by the experiment that the application of the method presented in this paper is more effective and practicable than the method with adopting the unique forgetting and incenting function for all interests.