Starting from data available about (anonymous) visitors of a website, possibilities of extracting information as well as enriching pure browsing patterns with the help of business measures (e-metrics) and detecting possibly negative effects of web robots are sketched as prerequisites for interpretation purposes of the navigational behavior of internet users. Problems with the reconstruction of user navigation paths are explained and different heuristics for path completion are discussed. Additionally, several kinds of features of user navigation paths (e.g., sets, sequences, path fragments) have to be mentioned to prepare for an adequate theoretical background concerning recommender systems that can be used for tasks as different as site personalization, cross-/up-selling, and navigation assistance. A vocabulary to describe different kinds of recommender systems and generic quality measures for system evaluation are formulated. Then, specific recommender systems, especially systems based on frequent path features, are defined and evaluated in a final experiment. In an outlook directions for future research on recommender systems are given.
