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User Modeling and User-Adapted Interaction 13

Editors:Alfred Kobsa
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Links:link.springer.com | Table of Contents
  1. UMUAI 2003-02 Volume 13 Issue 1/2
  2. UMUAI 2003-08 Volume 13 Issue 3
  3. UMUAI 2003-11 Volume 13 Issue 4

UMUAI 2003-02 Volume 13 Issue 1/2

Editorial BIBFull-Text 1-3
A Movie Recommendation System -- An Application of Voting Theory in User Modeling BIBAKFull-Text 5-33
  Rajatish Mukherjee; Neelima Sajja; Sandip Sen
Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback about its recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.
Keywords: pro-active information gathering; recommender system; text-based learning; user modeling; voting theory
A System for Building Intelligent Agents that Learn to Retrieve and Extract Information BIBAKFull-Text 35-88
  Tina Eliassi-Rad; Jude Shavlik
We present a system for rapidly and easily building instructable and self-adaptive software agents that retrieve and extract information. Our Wisconsin Adaptive Web Assistant (WAWA) constructs intelligent agents by accepting user preferences in the form of instructions. These user-provided instructions are compiled into neural networks that are responsible for the adaptive capabilities of an intelligent agent. The agent's neural networks are modified via user-provided and system-constructed training examples. Users can create training examples by rating Web pages (or documents), but more importantly WAWA's agents uses techniques from reinforcement learning to internally create their own examples. Users can also provide additional instruction throughout the life of an agent. Our experimental evaluations on a 'home-page finder' agent and a 'seminar-announcement extractor' agent illustrate the value of using instructable and adaptive agents for retrieving and extracting information.
Keywords: information extraction; information retrieval; instructable and adaptive software agents; machine learning; neural networks; Web mining
Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents BIBAKFull-Text 89-132
  Justine Cassell; Timothy Bickmore
Building a collaborative trusting relationship with users is crucial in a wide range of applications, such as advice-giving or financial transactions, and some minimal degree of cooperativeness is required in all applications to even initiate and maintain an interaction with a user. Despite the importance of this aspect of human--human relationships, few intelligent systems have tried to build user models of trust, credibility, or other similar interpersonal variables, or to influence these variables during interaction with users. Humans use a variety of kinds of social language, including small talk, to establish collaborative trusting interpersonal relationships. We argue that such strategies can also be used by intelligent agents, and that embodied conversational agents are ideally suited for this task given the myriad multimodal cues available to them for managing conversation. In this article we describe a model of the relationship between social language and interpersonal relationships, a new kind of discourse planner that is capable of generating social language to achieve interpersonal goals, and an actual implementation in an embodied conversational agent. We discuss an evaluation of our system in which the use of social language was demonstrated to have a significant effect on users' perceptions of the agent's knowledgableness and ability to engage users, and on their trust, credibility, and how well they felt the system knew them, for users manifesting particular personality traits.
Keywords: dialogue; embodied conversational agent; small talk; social interface; trust
Interactive Improvisational Music Companionship: A User-Modeling Approach BIBFull-Text 133-177
  Belinda Thom
Multi-Agent Multi-User Modeling in I-Help BIBAKFull-Text 179-210
  Julita Vassileva; Gordon McCalla; Jim Greer
This paper describes the user modeling approach applied in I-Help, a distributed multi-agent based collaborative environment for peer help. There is a multitude of user modeling information in I-Help, developed by the various software agents populating the environment. These 'user model fragments' have been created in a variety of specific contexts to help achieve various goals. They are inherently inconsistent with one another and reflect not only characteristics of the users, but also certain social relationships among them. The paper explores some of the implications of multi-agent user modeling in distributed environments.
Keywords: agent negotiation; decentralized; distributed user modeling; evaluation; expert finding; help-desk; just in time user modeling; modeling interpersonal relationships; multi-agent systems

UMUAI 2003-08 Volume 13 Issue 3

Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE BIBAKFull-Text 213-267
  Kyparisia A. Papanikolaou; Maria Grigoriadou
In this paper we present an Adaptive Educational Hypermedia prototype, named INSPIRE. The approach employed in INSPIRE emphasizes the fact that learners perceive and process information in very different ways, and integrates ideas from theories of instructional design and learning styles. Our aim is to make a shift towards a more 'learning-focused' paradigm of instruction by providing a sequence of authentic and meaningful tasks that matches learner' preferred way of studying. INSPIRE, throughout its interaction with the learner, dynamically generates learner-tailored lessons that gradually lead to the accomplishment of learner's learning goals. It supports several levels of adaptation: from full system-control to full learner-control, and offers learners the option to decide on the level of adaptation of the system by intervening in different stages of the lesson generation process and formulating the lesson contents and presentation. Both the adaptive and adaptable behavior of INSPIRE are guided by the learner model which provides information about the learner, such as knowledge level on the domain concepts and learning style. The learner model is exploited in multiple ways: curriculum sequencing, adaptive navigation support, adaptive presentation, and supports system's adaptable behavior. An empirical study has been performed to evaluate the adaptation framework and assess learners' attitudes towards the proposed instructional design.
Keywords: adaptability; adaptation; adaptive educational hypermedia systems; adaptive navigation support; adaptive presentation; adaptivity; curriculum sequencing; distance learning; instructional design; instructional strategies; learner control; learner model; learning styles
Probabilistic Student Modelling to Improve Exploratory Behaviour BIBAKFull-Text 269-309
  Andrea Bunt; Cristina Conati
This paper presents the details of a student model that enables an open learning environment to provide tailored feedback on a learner's exploration. Open learning environments have been shown to be beneficial for learners with appropriate learning styles and characteristics, but problematic for those who are not able to explore effectively. To address this problem, we have built a student model capable of detecting when the learner is having difficulty exploring and of providing the types of assessments that the environment needs to guide and improve the learner's exploration of the available material. The model, which uses Bayesian Networks, was built using an iterative design and evaluation process. We describe the details of this process, as it was used to both define the structure of the model and to provide its initial validation.
Keywords: adaptive feedback; Bayesian networks; exploration; open learning environments; student modelling

UMUAI 2003-11 Volume 13 Issue 4

Web Usage Mining as a Tool for Personalization: A Survey BIBAKFull-Text 311-372
  Dimitrios Pierrakos; Georgios Paliouras
This paper is a survey of recent work in the field of web usage mining for the benefit of research on the personalization of Web-based information services. The essence of personalization is the adaptability of information systems to the needs of their users. This issue is becoming increasingly important on the Web, as non-expert users are overwhelmed by the quantity of information available online, while commercial Web sites strive to add value to their services in order to create loyal relationships with their visitors-customers. This article views Web personalization through the prism of personalization policies adopted by Web sites and implementing a variety of functions. In this context, the area of Web usage mining is a valuable source of ideas and methods for the implementation of personalization functionality. We therefore present a survey of the most recent work in the field of Web usage mining, focusing on the problems that have been identified and the solutions that have been proposed.
Keywords: data mining; machine learning; personalization; user modeling; web usage mining
User Attitudes Regarding a User-Adaptive eCommerce Web Site BIBAKFull-Text 373-396
  Sherman R. Alpert; John Karat
Despite an abundance of recommendations by researchers and more recently by commercial enterprises for adaptive interaction techniques and technologies, there exists little experimental validation of the value of such approaches to users. We have conducted user studies focussed on the perceived value of a variety of personalization features for an eCommerce Web site for computing machinery sales and support. Our study results have implications for the design of user-adaptive applications. Interesting findings include unenthusiastic user attitudes toward system attempts to infer user needs, goals, or interests and to thereby provide user-specific adaptive content. Users also expressed equivocal opinions of collaborative filtering for the specific eCommerce scenarios we studied; thus personalization features popular in one eCommerce environment may not be effective or useful for other eCommerce domains. Users expressed their strong desire to have full and explicit control of data and interaction. Lastly, users want readily to be able to make sense of site behavior, that is, to understand a site's rationale for displaying particular content.
Keywords: adaptive interaction; collaborative filtering; eCommerce; human-computer interaction; personalization; user profile; user studies

Book Review

Book Review: Building Natural Language Generation Systems BIBFull-Text 397-401
  Roy Wilson