Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user’s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.

Web Usage Mining is the application of data mining techniques to Web clickstream data in order to extract usage patterns. As Web sites continue to grow in size and complexity, the results of Web Usage Mining have become critical for a number of applications such as Web site design, business and marketing decision support, personalization, usability studies, and network trac analysis. The two major challenges involved in Web Usage Mining are preprocessing the raw data to provide an accurate picture of how a site is being used, and ltering the results of the various data mining algorithms in order to present only the rules and patterns that are potentially interesting. This thesis develops and tests an architecture and algorithms for performing Web Usage Mining. An evidence combination framework referred to as the information lter is developed to compare and combine usage, content, and structure information about a Web site. The information lter automatically identi es the discovered ...

The core idea of the Semantic Web is to make information accessible to human and software agents on a semantic basis. Hence, web sites may feed directly from the Semantic Web exploiting the underlying structures for human and machine access. We have developed a generic approach for developing semantic portals, viz. SEAL (SEmantic portAL), that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal. In this paper, we discuss the role that semantic structures make for establishing communication between different agents in general. We elaborate on a number of intelligent means that make semantic web sites accessible from the outside, viz. semantics-based browsing, semantic querying and querying with semantic similarity, semantic personalization, and machine access to semantic information at a semantic portal. As a case study we refer to the AIFB web site --- a place that is increasingly driven by Semantic Web tec...

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.

Research at the University of Southampton has extended generalised Open Hypermedia (OH) models to include concepts of context and behaviour, both traditionally very important to the Adaptive Hypermedia (AH) domain. In this paper we re-evaluate Brusilovsky's pragmatic taxonomy of AH techniques from a structural perspective. A variety of OH structures are discussed that can be used to implement the techniques found in the taxonomy. By identifying common structures we gain a new perspective on the relationship between different AH techniques.

Consider a DAG (directed acyclic graph) G = (V, E) representing a collection V of web pages connected via links E. All web pages can be reached from a designated source page, represented by a source node s of G. Each web page carries a weight representative of the frequency with which it is visited. By adding hotlinks, at most one per page, we are interested in minimizing the expected number of steps needed to visit a selected set of web pages from the source page. For arbitrary DAGs we show that the problem is NP-complete. We also give algorithms for assigning hotlinks, as well as upper and lower bounds on the expected number of steps to reach the leaves from the source page s located at the root of the tree. Depending on the probability distribution (arbitrary, uniform, Zipf) the expected number of steps is at most c \Delta n, where c is a constant less than 1. For the geometric distribution we show how to obtain a constant average number of steps.

Most web services take a "one size fits all" approach: all visitors see the same generic content, formatted in the same generic manner.

In this paper we propose a WUM recommender system, called SUGGEST 3.0, that dynamically generates links to pages that have not yet been visited by a user and might be of his potential interest. Differently from the recommender systems proposed so far, SUGGEST 3.0 does not make use of any off-line component, and is able to manage Web sites made up of pages dynamically generated. To this purpose SUGGEST 3.0 incrementally builds and maintains historical information by means of an incremental graph partitioning algorithm, requiring no off-line component. The main innovation proposed here is a novel strategy that can be used to manage large Web sites. Experiments, conducted in order to evaluate SUGGEST 3.0 performance, demonstrated that our system is able to anticipate users' requests that will be made farther in the future, introducing a limited overhead on the Web server activity .

Markov models have been extensively used to model Web users' navigation behaviors on Web sites. The link structure of a Web site can be seen as a citation network. By applying bibliographic co-citation and coupling analysis to a Markov model constructed from a Web log file on a Web site, we propose a clustering algorithm called CitationCluster to cluster conceptually related pages. The clustering results are used to construct a conceptual hierarchy of the Web site. Markov model based link prediction is integrated with the hierarchy to assist users' navigation on the Web site.

During their navigation web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Sophisticated mining processes are needed for this knowledge to be extracted, understood and used. In this paper we propose a Web Usage Mining (WUM) system, called SUGGEST, designed to efficiently integrate the WUM process with the ordinary web server functionalities. It can provide useful information to make easier the web user navigation and to optimize the web server performance. Two quantities are introduced in order to give a measure of the quality of our WUM system.