Web usage mining has become the subject of intensive research, as its ... The first experiment concerned a specific KDD application and has shown the sensitivity of the heuristics to particularities of the site's structure and traffic. The second experiment is not bound to a specific application but rather compares the performance of the heuristics for different measures and thus for di erent application types. Our results show that there is no single best heuristic, but our measures help the analyst in the selection of the heuristic best suited for the application at hand.

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Effective web presence is for many companies indispensable for their success to the global market. In recent years, several methods have been developed for measuring and improving the eoeectiveness of commercial sites. However, they mostly concentrate on web page design and on access analysis. In this study, we propose a methodology of assessing the quality of a web site in turning its users into customers. Our methodology is based on the discovery and comparison of navigation patterns of customers and non-customers. This comparison leads to rules on how the site's topology should be improved. We further propose a technique for dynamically adapting the site according to those rules.

Abstract. For many companies, competitiveness in e-commerce requires a successful presence on the web. Web sites are used to establish the company’s image, to promote and sell goods and to provide customer support. The success of a web site affects and reflects directly the success of the company in the electronic market. In this study, we propose a methodology to improve the “success ” of web sites, based on the exploitation of navigation pattern discovery. In particular, we present a theory, in which success is modelled on the basis of the navigation behaviour of the site’s users. We then exploit WUM, a navigation pattern discovery miner, to study how the success of a site is reflected in the users ’ behaviour. With WUM we measure the success of a site’s components and obtain concrete indications of how the site should be improved. We report on our first experiments with an online catalog, the success of which we have studied. Our mining analysis has shown very promising results, on the basis of which the site is currently undergoing concrete improvements.

Usage patterns discovered through Web usage mining are effective in capturing item-to-item and user-to-user relationships and similarities at the level of user sessions Without the benefit of deeper domain knowledge, such patterns provide little insight into the underlying reasons for which such items or users are grouped together This can lead to a number of important shortcomings in personalization systems based on Web usage mining or collaborative filtering. For example, if a new item is recently added to the Web site, it is not likely that the pages associated with the item would be a part of any of the discovered patterns, and thus these pages cannot be recommended. Keyword-based content-filtering approaches have been used to enhance the effectiveness of collaborative filtering systems by focusing on content similarity among items or pages. These approaches, however, are incapable of capturing more complex relationships at a deeper semantic level based on different types of attributes associated with structured objects. This paper represents work-in-progress towards creating a general framework for using domain ontologies to automatically characterize usage profiles containing a set of structured Web objects. Our motivation is to use this framework in the context of Web personalization, going beyond page- or item-level constructs, and using the full semantic power of the underlying ontology.

Companies with web presence rely on web usage analysis to obtain insights on customer behavior,

The analysis of user behavior on the Web presupposes a reliable reconstruction of the users ’ navigational activities. Cookies and server-generated session identifiers have been designed to allow a faithful session reconstruction. However, in the absence of reliable methods, analysts must rely on heuristics methods (a) to identify unique visitors to a site, and (b) to distinguish among the activities of such users during independent sessions. The characteristics of the site, such as the site structure, as well as the methods used for data collection (e.g., the existence of cookies and reliable synchronization across multiple servers) may necessitate the use of different types of heuristics. In this study, we extend our work on the reliability of sessionizing mechanisms, by investigating the impact of site structure on the quality of constructed sessions. Specifically, we juxtapose sessionizing on a frame-based and a frame-free version of a site. We investigate the behavior of cookies, server-generated session identification, and heuristics that exploit session duration, page stay time and page linkage. Different measures of session reconstruction quality, as well as experiments on the impact on the prediction of frequent entry and exit pages, show that different reconstruction heuristics can be recommended depending on the characteristics of the site. We also present first results on the impact of session reconstruction heuristics on predictive applications such as Web personalization.

Web personalization is the process of customizing a Web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user's navigational behavior. Integrating usage data with content, structure or user profile data enhances the results of the personalization process. In this paper, we present SEWeP, a system that makes use of both the usage logs and the semantics of a Web site's content in order to personalize it. Web content is semantically annotated using a conceptual hierarchy (taxonomy). We introduce C-logs, an extended form of Web usage logs that encapsulates knowledge derived from the link semantics. C-logs are used as input to the Web usage mining process, resulting in a broader yet semantically focused set of recommendations.

In data models that have graph representations, users navigate following the links of the graph structure. Conducting data mining on collected information about user accesses in such models, involves the determination of frequently occurring access sequences. In this paper, we examine the problem of finding traversal patterns from such collections. The determination of patterns is based on the graph structure of the model. For this purpose, we present three algorithms, one which is level-wise with respect to the lengths of the patterns and two which are not. Additionally, we consider the fact that accesses within patterns may be interleaved with random accesses due to navigational purposes. The definition of the pattern type generalizes existing ones in order to take into account this fact. The performance of all algorithms and their sensitivity to several parameters is examined experimentally.

This paper presents a framework for enhancing Web usage records with formal semantics based on an ontology underlying the site. Besides, it elicits automated methods of mapping URLs to application events. Using the ontology's taxonomy, we describe user actions at different levels of abstractions. Using the ontology's concepts and relations, we capture the multitude of user interests expressed by a visit to one page. We employ our ideas in an application of SEAL, a framework for semantic portals that uses Semantic Web technologies to support communities of interest. Different realizations of semantically enriched user tracking are discussed and related to other approaches. We describe first results from a prototypical system, and discuss benefits of Conceptual User Tracking for Web usage mining