Computer Engineering

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.

The amounts of information residing on web sites make users' navigation a hard task. To address this problem, web sites provide recommendations to the end users, based on similar users' navigational patterns mined from past visits. In this paper we introduce a recommendation method, which integrates usage data recorded in web logs, and the conceptual relationships between web documents. In the proposed framework, the usage-oriented URI representation of web pages and users' behavior is augmented with content-based semantics expressed using domainontology terms. Since the number of multilingual web sites is constantly increasing, we also propose an automatic method for uniformly characterizing a web site's documents using a common vocabulary. Both methods are integrated in the semantic web personalization system SEWeP.

Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users' navigation is used to extract popular web paths and predict current users' next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important -in terms of pagerank authority score -paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

Recommendation algorithms aim at proposing "next" pages to a user based on her current visit and the past users' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a novel personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site's structure and its usage data we produce personalized navigational graph synopses (prNG) to be used for applying UPR and produce personalized recommendations. Experimental results show that the accuracy of the recommendations is superior to pure usage-based approaches.

Web sites have become an increasingly important part of every country's information and cultural heritage. For this reason, Web archiving has become an issue for many national libraries. In this paper, we present a first attempt to archive the Greek Web. This project is divided in two parts; the first part concerns the collection of the majority of Greek Web pages. The second part focuses on the knowledge extraction from this archive, in order to classify it in semantically coherent clusters. Considerations concerning the criteria that should be set in order to characterize a Web page as "Greek" are discussed. A combination of IR and content mining techniques is applied in order to semantically characterize the collected content. We especially address the bilingualism issue arising because the content is written in both Greek and English. The collected Web pages are finally classified into meaningful clusters, facilitating the searching of the archive.

Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. To assist users in this context, we draw inspiration from Web recommender systems and propose the use of personalized query recommendations. The idea is to track the querying behavior of each user, identify which parts of the database may be of interest for the corresponding data analysis task, and recommend queries that retrieve relevant data. We discuss the main challenges in this novel application of recommendation systems, and outline a possible solution based on collaborative filtering. Preliminary experimental results on real user traces demonstrate that our framework can generate effective query recommendations.

Web personalization is the process of customizing a web site to the needs of each specific user or set of users. Personalization of a web site may be performed by the provision of recommendations to the users, highlighting/adding links, creation of index pages, etc. The web personalization systems are mainly based on the exploitation of the navigational patterns of the web site's visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. The exploitation of the web pages' semantics can considerably improve the results of web usage mining and personalization, since it provides a more abstract yet uniform and both machine and human understandable way of processing and analyzing the usage data. The underlying idea is to integrate usage data with content semantics, expressed in ontology terms, in order to produce semantically enhanced navigational patterns that can subsequently be used for producing valuable recommendations. In this paper we propose a semantic web personalization system, focusing on word sense disambiguation techniques which can be applied in order to semantically annotate the web site's content.

The World Wide Web, has grown in the past few years from a small research community to the biggest and most popular way of communication and information dissemination. Every day, the WWW grows by roughly a million electronic pages, adding to the hundreds of millions already on-line. WWW serves as a platform for exchanging various kinds of information, ranging from research papers, and educational content, to multimedia content and software.

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users' needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing "next" pages to users based on their current visit and the past users' navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important -in terms of PageRank authority score -pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web site's navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational sub-graphs for online web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by tracking their querying behavior and generating personalized query recommendations. The system is supported by two recommendation engines and the underlying recommendation algorithms. The first identifies potentially "interesting" parts of the database related to the corresponding data analysis task by locating those database parts that were accessed by similar users in the past. The second identifies structurally similar queries to the ones posted by the current user. Both approaches result in a recommendation set of SQL queries that is provided to the user to modify, or directly post to the database. The demonstrated system will enable users to query and get real-time recommendations from the SkyServer database, using user traces collected from the SkyServer query log.

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. The objective of the I-KnowUMine project (IKUM) is to develop an integrated platform (referred to in the paper as the "IKUM system") that uses state of the art technology and research results from different application domains in order to provide the basis for the development of online services in a wide range of application areas, presenting personalized content, services and applications to users in a structure more suited to their needs. The benefits provided by the IKUM system result mainly from the combination and integration of technology advances in areas such as Web Mining, Content Management, Personalization and Portals. As a result of this novel combination of these technologies, users of the IKUM system will benefit from the optimal logical structure of information/content provided by the system, allowing them to efficiently execute their processes and to reach their information targets.

As the Web continuously grows, the results returned by search engines are too many to review. Lately, the problem of personalizing the ranked result list based on user feedback has gained a lot of attention. Such approaches usually require a big amount of user feedback on the results, which is used as training data. In this work, we present a method that overcomes this issue by exploiting all search results, both rated and unrated, in order to train a ranking function. Given a small initial set of user feedback for some search results, we first perform clustering on all results returned by the search. Based on the clusters created, we extend the initial set of rated results, including new, unrated results. Then, we use a popular training method (Ranking SVM) to train a ranking function using the expanded set of results. The experiments show that our method approximates sufficiently the results of an "ideal" system where all results of each query should be rated in order to be used as training data, something that is not feasible in a real-world scenario.

In this paper we present FGP, an algorithm that combines the powers of an association rule mining algorithm (FP-Growth) and a generalized pattern mining algorithm (GP-Close) in order to efficiently generate rules from transaction data. Our Frequent Generalized Pattern (FGP) algorithm considers that all items that appear in a set of transactions, belong to categories organized in a taxonomy. It takes as input the transaction database and the taxonomy of categories and produces generalized association rules that contain transaction items and/or item categories. This algorithm is particularly useful for personalizing web sites with continuously updated content, such as, blog aggregators, or news portals. In this context, the transaction database contains user click-stream information and the hierarchy of item types is a thematic taxonomy of web pages. The algorithm generates frequent itemsets comprising of both web pages and categories. The results are used to generate association rules and consequently recommendations for the users. We experimentally evaluate the proposed algorithm using web log data collected from a newspaper web site.

Recommendation algorithms aim at proposing "next" pages to a user based on her navigational behavior. In the vast majority of related algorithms, only the usage data are used to produce recommendations. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site's structure and previously recorded user sessions we produce personalized navigational subgraphs (prNGs) to be used for applying UPR. Experimental results show that the accuracy of the generated recommendations is superior to pure usage-based approaches.

In this paper we present FGP, an algorithm that combines the powers of an association rule mining algorithm (FP-Growth) and a generalized pattern mining algorithm (GP-Close) in order to efficiently generate rules from transaction data. Our Frequent Generalized Pattern (FGP) algorithm considers that all items that appear in a set of transactions, belong to categories organized in a taxonomy. It takes as input the transaction database and the taxonomy of categories and produces generalized association rules that contain transaction items and/or item categories. We demonstrate the operation of the proposed algorithm in the context of personalizing a web site. In this context, the transaction database contains user clickstream information and the hierarchy of item types is a thematic taxonomy of web pages. The algorithm generates frequent itemsets comprising of both web pages and categories. The results can be used to generate association rules and consequently recommendations for the users. We experimentally evaluate the proposed algorithm using web log data collected from a newspaper web site.