Web usage mining based on probabilistic latent semantic analysis

The primary goal of Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of user sessions and discovering association rules or frequent navigational paths, do not generally provide the ability to automatically characterize or quantify the unobservable factors that lead to common navigational patterns. It is, therefore, necessary to develop techniques that can automatically identify the users' underlying navigational objectives and to discover hidden semantic relationships among users as well as between users and Web objects. Probabilistic Latent Semantic Analysis (PLSA) is particularly useful in this context, since it can uncover latent semantic associations among users and pages based on the co-occurrence patterns of these pages in user sessions. In this paper, we develop a unified framework for the discovery and analysis of Web navigational patterns based on PLSA. We show the flexibility of this framewo...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Xin Jin, Yanzan Zhou, Bamshad Mobasher
• Keywords: Common Navigational Patterns, Data Mining, Frequent Navigational Paths, KDD 2004, Web Navigational Patterns