Clustering techniques have been used by manyintelligent software agents in order to retrieve, lter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document structures to de ne a distance or similarity among these documents, or use probabilistic techniques such as Bayesian classi cation. Many of these traditional algorithms, however, falter when the dimensionality of the feature space becomes high relative to the size of the document space. In this paper, we introduce two new clustering algorithms that can e ectively cluster documents, even in the presence of a very high dimensional feature space. These clustering techniques, which are based on generalizations of graph partitioning, do not require pre-speci ed ad hoc distance functions, and are capable of automatically discovering document similarities or associations. We conduct several experiments on real Web data using various feature selection heuristics, and compare our clustering schemes to standard distance-based techniques, such ashierarchical agglomeration clustering, and Bayesian classi cation methods, such as AutoClass.

Abstract. We describe the user modeling and personalization techniques adopted in SETA, a shell supporting the construction of adaptive Web stores which customize the interactions with users, suggesting the items best fitting their needs, and adapting the description of the store catalog to their preferences and expertise. SETA uses stereotypical information to handle the user models and applies personalization rules to dynamically generate the hypertextual pages presenting products: the system adapts the graphical aspect, length and terminology used in the descriptions to the user’s receptivity, expertise and interests. Moreover, it maintains a profile associated to each person the goods are selected for, to provide multiple criteria for the selection of items, tailored to the beneficiaries ’ preferences. 1

With the recent expansion of the Internet, the interest towards electronic sales has quickly grown and many tools have been built to help vendors to set up their Web stores. These tools offer all the facilities for building the store databases and managing the order processing and secure payment transactions, but they typically do not focus on issues like the personalization of the interaction with the customers. However, Web surfers are generally heterogeneous and have different needs and preferences; moreover, the trend of marketing strategies is to pay more and more attention to the specific buyers. So, the importance of personalizing the interaction with the user and the product presentation is increasing. In this paper, we describe the architecture of a configurable virtual Web store supporting personalized hypertextual interactions with users. Our system builds a user profile by applying user modeling techniques and stereotypical information about the characteristics of customer groups; this profile is used during the interaction in order to tailor the product descriptions and the selection of items to recommend to the user's needs, varying the layout of the hypertextual pages and the detail of the descriptions accordingly. Tailoring the system's behavior requires the parallel execution of several complex tasks during the interaction (e.g. identifying the user's preferences, selecting the products most suited to her, dynamically generating the hypertextual pages). Therefore, we have defined a multiagent architecture where these tasks

This paper presents a possible solution for the problem of integrating contextual knowledge in the WordNet database. Contextual structures are derived from three sources: (1) minimal contexts - in the form of semantic nets transformations of WordNet glosses; (2) dynamic contexts rendered by webs of lexico-semantic paths revealing textual implied information and (3) static contexts - represented by patterns of concepts and semantic links. The relevance of these structures is measured on a three-tired benchmark, comprising (a) word-sense disambiguation; (b) coreference resolution and (c) acquisition of domain patterns for information extraction. 1 The Basic Idea Recently, a new version of the WordNet lexical database [Miller, 1995] developed at Princeton has become publicly available (www.cogsci.princeton.edu/~wn). WordNet 1.6 contains 126,520 English words grouped into 91,595 synonym sets, called synsets. Words and synsets are entangled by 391,885 lexico-semantic relations, making Word...

We propose an unsupervised divisive partitioning algorithm for document data sets which enjoys many favorable properties. In particular, the algorithm shows excellent scalability to large data collections and produces high quality clusters which are competitive with other clustering methods. The algorithm yields information on the significant and distinctive words within each cluster, and these words can be inserted into the naturally occuring hierarchical structure produced by the algorithm. The result is an automatically generated hierarchical topical taxonomy of a document set. In this paper, we show how the algorithm's cost scales up linearly with the size of the data, illustrate experimentally the quality of the clusters produced, and show how the algorithm can produce a hierarchical topical taxonomy.

In the last years, the amount of information stored in Internet has grown exponentially. This article presents a new approach to cooperative problem solving that use the Web as a source of data. The architecture has been designed using two main Artificial Intelligence techniques: Multiagent System design, and problem solving (planning). Both are used to obtain a new architecture that dynamically obtains knowledge from Internet. The system uses two different types of agents: planning agents and web agents. Planning agents pay attention to the user's queries and solve his/her problems at a high level of abstraction; web agents fill in the details obtaining the required information from Internet. Different partial solutions given by the web agents while combined by the planning agent to obtain a detailed solution (or solutions) to the user queries.

The undi erentiated approach to mass marketing has been recently replaced with market segmentation techniques: psychographic and socio-demographic analyses are exploited to identify homogeneous market segments to which suitable

. We describe the user modeling and personalization techniques adopted in SETA, a shell supporting the construction of adaptive Web stores which customize the interactions with users, suggesting the items best fitting their needs, and adapting the description of the store catalog to their preferences and expertise. SETA uses stereotypical information to handle the user models and applies personalization rules to dynamically generate the hypertextual pages presenting products: the system adapts the graphical aspect, length and terminology used in the descriptions to the user's receptivity, expertise and interests. Moreover, it maintains a profile associated to each person the goods are selected for, to provide multiple criteria for the selection of items, tailored to the beneficiaries' preferences. 1 Introduction With the expansion of Internet, tools have been built to help vendors to set up Web stores, building the store databases and managing the order processing and payment...

We describe the architecture of a configurable Web store supporting personalized interactions with users. Our system exploits user modeling and flexible hypermedia techniques to tailor to the user the suggestion of goods and the description of the store catalog. Customizing the system's behavior requires the parallel execution of several complex tasks during the interaction (e.g. identifying the user's preferences and dynamically generating the hypertextual pages of the store catalog). We argue that only an architecture composed of specialized agents can successfully carry on these tasks. Our system is developed in a Java-based environment using tools for building interoperable, agent-based systems (e.g. JDBC drivers for database independence; Voyager for the multiagent architecture). The system architecture includes an instance of each specialized agent and an agent which enables the communication among the others. 1. INTRODUCTION In this paper, we describe a tool to build adaptive...

The Internet, in particular the World Wide Web, contains a vast amount of data in the form of HTML documents, text files, multimedia files and other resources. The content of web resources is not always clear to a user, or a search engine, without actually looking at page itself. Using machine-learning techniques to identify web user behavior from web server logs can guide the user towards resources that are relevant as well as helping web publishers to structure web-sites to suit the visitors. Here we describe a learning agent that works from the server point of view and tracks users' behavior: WebProfile.Users' behaviors are the basis for issuing on the fly recommendations to website visitors according to their apparent interests. WebProfile is currently based on Bayesian user profiles that are being elaborated intro structured objects similar to Bayesian networks so as to give a better account of user's trajectories inside a the web site. Both approaches are commented and compared. Keywords: Learning Agents, Web Agent, Bayesian classification, Bayesian Networks, Data mining, World Wide Web. This work has been supported by project CICYT-TIC960878 of the Spanish Science and Technology Commission 1 1