Current search technologies work in "one size fits all" fashion. Therefore, the answer to a query is independent of specific user information need. In this paper, we describe a novel ranking technique for personalized search services that combines content-based and community-based evidences. The community-based information is used in order to provide context for queries and is influenced by the current interaction of the user with the service. Our algorithm is evaluated using data derived from an actual service available on the Web, an online bookstore. We show that the quality of content-based ranking strategies can be improved by the use of community information as another evidential source of relevance. In our experiments, the improvements reach up to 48% in terms of average precision.

In this paper we analyze the performance of clustering methods on the task of constructing community models for the users of large Web sites.

The rapid evolution of interactive Internet services has led to both a constantly increasing number of modern web sites and to an increase in their functionality, which in turn makes them more complicated to use. The COGITO project aims at developing innovative software components allowing e-commerce companies to effectively set up and maintain Web sites which address customers in personalized and pro-active ways. The COGITO solution is based on "intelligent personalized agents" which represent virtual assistants or advisors (also visually) by modeling their ability to support customers. In this paper we present the profile extractor, the personalization component, based on machine learning techniques, which allows for the discovery of preferences, needs and interests of users that have access to an e-commerce web site. Exploiting personalization and the underlying `one-to-one' marketing paradigm is of great importance for business in order to be successful in today competitive markets.

Until recently, electronic markets were dominated by the combination of static offer plus fixed pricing policies. Static offer schemes assume that all users have the same criteria which may not meet the requirements of all potential buyers. A fixed price might not always reflect the current market balance of supply and demand and the specific valuation of a single buyer. In this paper we propose an agent-mediated insurance brokering system using a flexible negotiation model that includes multi-attribute bidding as well as some kind of learning capabilities.

Common experience suggests that users of online services can be grouped into communities on the basis of interest. Much of the recent research on algorithms for identification of communities in the Web has focused on techniques that rely on link structures. In this paper, we describe a new technique for discovering communities of interest in Web services. Instead of relying upon link structures, we propose an algorithm based on user access behavior. A graph structure is created based on the user access patterns. This structure is shown to have useful properties for community discovery. We apply the algorithm to a synthetic dataset, known to show interest-based community structure, and use the results to compare our algorithm against other methodologies. We also apply the algorithm to two real world online services: a bookstore and an online radio. The case studies are relevant because they emphasize the contribution of the algorithm to find out communities in an environment without explicit structures that represent relationships among users.

In this paper we present an approach to the discovery of trends in the usage of large Web-based information systems. This approach is based on the empirical analysis of the users interaction with the system and the construction of user groups with common interests (user communities). The empirical analysis is achieved with the use of cluster mining, a technique that process data collected from the users' interaction with the Web site. Our main concern is the construction of meaningful communities, which can be used for improving the structure of the site as well as for making suggestions to the users at a personal level. Our case study on a site providing information for researchers in Chemistry shows that the proposed method provides effective mining of large usage databases.