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.

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.

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.

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 identify the users' underlying navigational objectives and to 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 e#ectiveness our approach through experiments performed on several real-world data sets.

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.

this paper we study the impact of various preprocessing techniques applied to clickstream data,such as clustering, normalization,and significance filtering, on collaborative filtering. Our experimental results,performed on real usage data,indicate that with proper data preparation,the clustering-based approach to collaborative filtering can achieve dramatic improvements in terms of recommendation e#ectiveness,while maintaining the computational advantage over the direct approaches such as the k-Nearest- Neighbor technique

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

Web recommender systems anticipate the needs of web users and provide them with recommendations to personalize their navigation.

Abstract. In this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and preprocessing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly for Web personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety

Abstract. In this chapter we provide a comprehensive overview of the topic of Intelligent Techniques for Web Personalization. Web Personalization is viewed as an application of data mining and machine learning techniques to build models of user behaviour that can be applied to the task of predicting user needs and adapting future interactions with the ultimate goal of improved user satisfaction. This chapter survey’s the state-of-the-art in Web personalization. We start by providing a description of the personalization process and a classification of the current approaches to Web personalization. We discuss the various sources of data available to personalization systems, the modelling approaches employed and the current approaches to evaluating these systems. A number of challenges faced by researchers developing these systems are described as are solutions to these challenges proposed in literature. The chapter concludes with a discussion on the open challenges that must be addressed by the research community if this technology is to make a positive impact on user satisfaction with the Web. 1