GUMSAWS: A Generic User Modeling Server for Adaptive Web Systems

In this paper we focus on the architecture, design and implementation of a generic user modeling server for adaptive web systems (GUMSAWS), reaching the goals of generality, extendability and replaceability. GUMSAWS acts as a centralized user modeling server to assist several adaptive Web systems (possibly in different domains) concurrently. It incrementally builds up user models, provides functions of storing, updating and deleting entries in user profiles, and maintains consistency of user models. Our system is also able to infer missing entries in user profiles from different information sources, including direct information, groups information, association rules and general facts. We further evaluate its inference performance within the context of e-commerce. Experimental results show that the average accuracy of inferring user missing property values from different information resources is found to be almost 70%. We also use a personalized electronic news system to demonstrate the example of our system in use.