Information Searching Behavior Mining Based on Reinforcement Learning Models

Abstract

Mining the behavioral characteristics and the adaptive learning mechanism of users during their information searching is meaningful for the academic database providers to improve their service and build e-learning platform to help users manipulate their search products more effectively The paper comprises four main parts: the first, Related work, makes a literature review and declares the concerns of our research; the second, Theories and models, explains the basic idea of reinforcement learning behavior, and introduces three representative reinforcement learning models, i.e. BM model, BS model and CR model; the third, Experiments and analysis, experimentally observes the characteristics of the academic users’ reinforcement learning in the process of search tasks performing, further quantitatively simulates their reinforcement learning behavior in information seeking using the three learning models, and gives extensive discussions about these models; and the fourth, Conclusions, makes some suggestions for the academic database providers efficiently. Based on the theories and models of reinforcement learning behavior, this research takes the freshmen and senior students from universities as user samples, experimentally observes the explicit behavioral and implicit psychological characteristics of their learning behavior in the process of search tasks performing, and further quantitatively simulates their reinforcement learning behavior in information seeking using the Bush-Mosteller model, Borgers-Sarinare model and Cross model. Finally, the paper makes some extensive discussions about these models and gives some advices to the database providers.