A Unified Approach to Personalization Based on Probabilistic Latent Semantic Models of Web Usage and Content
Xin Yu Jin, Yanzan Zhou, Bamshad Mobasher · 2004
Web usage mining techniques, such as clustering of user sessions, are often used to identify Web user access patterns. However, to understand the factors that lead to common navigational patterns, it is necessary to develop techniques that can automatically characterize users’ navigational tasks and intentions. Such a characterization must be based both on the common usage patterns, as well as on common semantic information associated with the visited Web resources. The integration of semantic content and usage patterns allows the system to make inferences based on the underlying reasons for which a user may or may not be interested in particular items. In this paper, we propose a unified framework based on Probabilistic Latent Semantic Analysis to create models of Web users, taking into account both the navigational usage data and the Web site content information. Our joint probabilistic model is based on a set of discovered latent factors that “explain” the underlying relationships among pageviews in terms of their common usage and their semantic relationships. Based on the discovered user models, we propose algorithms for characterizing Web user segments and to provide dynamic and personalized recommendations based on these segments. Our experiments, performed on real usage data, show that this approach can more accurately capture users’ access patterns and generate more effective recommendations, when compared to more traditional methods based on clustering.