Web Usage Mining is the application of data mining techniques for discovery and analysis of user access patterns from the interaction generated by the users in the form of access logs, proxy-server logs, browser logs, etc. One major application is the use of these patterns in prediction and recommender systems to help personalize user browsing tasks. Web usage mining consists of three phases, namely preprocessing, pattern discovery and pattern analysis. This talk will first provide an overview of the web usage mining and recommendation processes, and then briefly describe some of the issues addressed in the research carried out with my students at Concordia.
