| Editorial | | BIB | Full-Text | 1-3 | |
| A Movie Recommendation System -- An Application of Voting Theory in User Modeling | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIB | Full-Text | 133-177 | |
| Belinda Thom | |||
| Multi-Agent Multi-User Modeling in I-Help | | BIBAK | Full-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 | |||
| Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE | | BIBAK | Full-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 | | BIBAK | Full-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 | |||
| Web Usage Mining as a Tool for Personalization: A Survey | | BIBAK | Full-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 | | BIBAK | Full-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: Building Natural Language Generation Systems | | BIB | Full-Text | 397-401 | |
| Roy Wilson | |||