document: 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 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 framework in characterizing various relationships among users and Web objects . Since these relationships are measured in terms of probabilities , we are able to use probabilistic inference to perform a variety of analysis tasks such as user segmentation , page classification , as well as predictive tasks such as collaborative recommendations . We demonstrate the effectiveness of our approach through experiments performed on real-world data sets .
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