Mining User Interests in Web Logs of an Online News Service Based on Memory Model

User profiling plays an important role in online news recommendation systems. In this paper, we analyze the relationship between users' clicking behaviors and the category of the news story to model user's interests by mining web log data of an adaptive news system. We train a Memory-based User Profile (MUP), which imitates human being's learning, remembering and forgetting mechanisms, to predict users' potential interests dynamically. We mainly focus on experimental analysis to refine the MUP scheme. Firstly, we materialize the meanings of all parameters of MUP by important factors (i.e., absorbing factor, forgetting factor, timescale and learning strength) in human being's learning and forgetting process. Secondly, we demonstrate how to determine the values of parameters for different users to reflect their distinct learning and forgetting abilities. Thirdly, we derive a threshold from MUP's recursion formula, which can be used to simply distinguish long-term and short-term interests. Our evaluations are carried out on IdoIcan's web log data, results show MUP can model user's profile effectively.