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We survey the major techniques for information retrieval. In the rst part, weprovide an overview of the traditional ones (full text scanning, inversion, signature les and clustering). In the second part we discuss attempts to include semantic information (natural language processing, latent semantic indexing and neural networks).

Abstract. Empirical evaluations are needed to determine which users are helped or hindered by user-adapted interaction in user modeling systems. A review of past UMUAI articles reveals insuf¢cient empirical evaluations, but an encouraging upward trend. Rules of thumb for experimental design, useful tests for covariates, and common threats to experimental validity are presented. Reporting standards including effect size and power are proposed. Key words: empirical evaluation, experimental design, covariant variables, e¡ect size, treatment magnitude, power, sensitivity. 1. What Is Empirical Evaluation? Empirical evaluation refers to the appraisal of a theory by observation in experiments. The key to good empirical evaluation is the proper design and execution of the experiments so that the particular factors to be tested can be easily separated from other confounding factors. For example, one may want to test whether a software system with a user model works better than the same system without a user model, test the effect of different levels of user modeling or different user model parameter settings, or test different user interfaces. These factors, which

Doppelg anger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. Doppelg anger's learning is called heterogeneous for two reasons: first, multiple learning techniques are used to interpret the data, and second, the learning techniques must often grapple with disparate data types. These computations take place at geographically distributed sites, and make use of portable user models carried by individuals. This paper concentrates on Doppelg anger's learning techniques and their implementation in an application-independent, sensor-independent environment. Key words: User model, machine learning, server-client architecture, multivariate statistical analysis, Markov models, Beta distribution, linear prediction. 1 Introduction When users interact with a computer, they provide a great deal of information about themselves. Even when they are not physically at a computer console,...

User modeling has made considerable progress during its existence now of more than a decade. In this paper, a survey of recent developments will be presented, which concentrates on the modeling of a user's knowledge, plans, and preferences in a domain, on the exploitation of new sources of information about the user, on issues of representation, inference and revision, on user modeling shell systems and servers, and on the verification of the practical utility of user models. Research trends and research deficiencies in these areas will be outlined, and potential risks described. 1. Introduction User modeling has made considerable progress during its existence now of more than a decade. Particularly in the last few years, the need for software systems to automatically adapt to their current users has been recognized in many application areas. Consequently, research on user modeling (which originated in the field of natural-language dialog systems) has spread into many disciplines whi...

This paper introduces the special issue of Knowledge-Based Systems on HumanComputer Collaboration (HCC). It derives a set of fundamental issues from a definition of collaboration, introduces two major approaches to HCC, and surveys each approach, showing how it formulates and addresses the issues. It concludes by proposing some themes that should characterize a unified approach to human-computer collaboration. 1 Introduction Collaboration is a process in which two or more agents work together to achieve shared goals. Thirty researchers came together in Raleigh, North Carolina in October of 1993 for a AAAI Fall Symposium dedicated to this topic. The goal of the symposium was to achieve a better understanding of Human-Computer Collaboration (HCC), collaboration involving at least one human and one computational agent. In particular, the symposium sought to explore the fundamental nature of collaborative problem solving, understand the constraints brought to bear by the differing charac...

Recently, Artificial Intelligence techniques have proved useful in helping users to handle the large amount of information on the Internet. The idea of personalized search engines, intelligent software agents, and recommender systems has been widely accepted among users who require assistance in searching, sorting, classifying, filtering and sharing this vast quantity of information. In this paper, we present a state-of-the-art taxonomy of intelligent recommender agents on the Internet. We have analyzed 37 different systems and their references and have sorted them into a list of 8 basic dimensions. These dimensions are then used to establish a taxonomy under which the systems analyzed are classified. Finally, we conclude this paper with a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.

Several current support systems for travel and tourism are aimed at providing information in a personalized manner, taking users interests and preferences into account. In this vein, personalized systems observe users behavior and, based thereon, make generalizations and predictions about them. This article describes a user modeling server that offers services to personalized systems with regard to the analysis of user actions, the representation of assumptions about the user, and the inference of additional assumptions based on domain knowledge and characteristics of similar users. The system is open and compliant with major standards, allowing it to be easily accessed by clients that need personalization services.

Locating software items is difficult, even for knowledgeable software designers, when searching in large, complex and continuously growing libraries. This paper describes a technique, we term active browsing. An active browser suggests to the designer items it estimates to be close to the target of the search. The novel aspect of active browsing is that it is entirely unobtrusive: it infers its similarity measure from the designer's normal browsing actions, without any special input. Experiments are presented in which the active browsing system succeeds 40% of the time in identifying the target before the designer has found it. An additional experiment indicates that this approach does, indeed, speed-up search.

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