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User Modeling and User-Adapted Interaction 17

Editors:Alfred Kobsa
Dates:2007
Volume:17
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:16
Links:link.springer.com | Table of Contents
  1. UMUAI 2007-03 Volume 17 Issue 1/2
  2. UMUAI 2007-07 Volume 17 Issue 3
  3. UMUAI 2007-09 Volume 17 Issue 4
  4. UMUAI 2007-12 Volume 17 Issue 5

UMUAI 2007-03 Volume 17 Issue 1/2

Special Issue on Statistical and Probabilistic Methods for User Modeling

Introduction to the special issue on statistical and probabilistic methods for user modeling BIBFull-Text 1-4
  David Albrecht; Ingrid Zukerman
A hybrid approach for improving predictive accuracy of collaborative filtering algorithms BIBAKFull-Text 5-40
  George Lekakos; George M. Giaglis
Recommender systems represent a class of personalized systems that aim at predicting a user's interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.
Keywords: Recommender systems; Collaborative filtering; Personalization; Lifestyle
Efficient and non-parametric reasoning over user preferences BIBAKFull-Text 41-69
  Carmel Domshlak; Thorsten Joachims
We consider the problem of modeling and reasoning about statements of ordinal preferences expressed by a user, such as monadic statement like "X is good," dyadic statements like "X is better than Y," etc. Such qualitative statements may be explicitly expressed by the user, or may be inferred from observable user behavior. This paper presents a novel technique for efficient reasoning about sets of such preference statements in a semantically rigorous manner. Specifically, we propose a novel approach for generating an ordinal utility function from a set of qualitative preference statements, drawing upon techniques from knowledge representation and machine learning. We provide theoretical evidence that the new method provides an efficient and expressive tool for reasoning about ordinal user preferences. Empirical results further confirm that the new method is effective on real-world data, making it promising for a wide spectrum of applications that require modeling and reasoning about user preferences.
Keywords: Preference elicitation; Ordinal utility function; Reasoning over preferences; Support vector machines; Kernels
Personalizing influence diagrams: applying online learning strategies to dialogue management BIBAKFull-Text 71-91
  David Maxwell Chickering; Tim Paek
We consider the problem of adapting the parameters of an influence diagram in an online fashion for real-time personalization. This problem is important when we use the influence diagram repeatedly to make decisions and we are uncertain about its parameters. We describe learning algorithms to solve this problem. In particular, we show how to modify various explore-versus-exploit strategies that are known to work well for Markov decision processes to the more general influence-diagram model. As an illustration, we describe how our techniques for online personalization allow a voice-enabled browser to adapt to a particular speaker for spoken dialogue management. We evaluate all the explore-versus-exploit strategies in this domain.
Keywords: Personalization; Influence diagrams; User-model adaptation; Planning; Dialogue management; Speech recognition
Improving command and control speech recognition on mobile devices: using predictive user models for language modeling BIBAKFull-Text 93-117
  Tim Paek; David Maxwell Chickering
Command and control (C&C) speech recognition allows users to interact with a system by speaking commands or asking questions restricted to a fixed grammar containing pre-defined phrases. Whereas C&C interaction has been commonplace in telephony and accessibility systems for many years, only recently have mobile devices had the memory and processing capacity to support client-side speech recognition. Given the personal nature of mobile devices, statistical models that can predict commands based in part on past user behavior hold promise for improving C&C recognition accuracy. For example, if a user calls a spouse at the end of every workday, the language model could be adapted to weight the spouse more than other contacts during that time. In this paper, we describe and assess statistical models learned from a large population of users for predicting the next user command of a commercial C&C application. We explain how these models were used for language modeling, and evaluate their performance in terms of task completion. The best performing model achieved a 26% relative reduction in error rate compared to the base system. Finally, we investigate the effects of personalization on performance at different learning rates via online updating of model parameters based on individual user data. Personalization significantly increased relative reduction in error rate by an additional 5%.
Keywords: Command and control; Language modeling; Speech recognition; Predictive user models
Adaptive testing for hierarchical student models BIBAKFull-Text 119-157
  Eduardo Guzmán; Ricardo Conejo
This paper presents an approach to student modeling in which knowledge is represented by means of probability distributions associated to a tree of concepts. A diagnosis procedure which uses adaptive testing is part of this approach. Adaptive tests provide well-founded and accurate diagnosis thanks to the underlying probabilistic theory, i.e., the Item Response Theory. Most adaptive testing proposals are based on dichotomous models, where he student answer can only be considered either correct or incorrect. In the work described here, a polytomous model has been used, i.e., answers can be given partial credits. Thus, models are more informative and diagnosis is more efficient. This paper also presents an algorithm for estimating question characteristic curves, which are necessary in order to apply the Item Response Theory to a given domain and hence must be inferred before testing begins. Most prior estimation procedures need huge sets of data. We have modified preexisting procedures in such a way that data requirements are significantly reduced. Finally, this paper presents the results of some controlled evaluations that have been carried out in order to analyze the feasibility and advantages of this approach.
Keywords: ITS; Student diagnosis; Adaptive testing; Item response theory; Statistical kernel smoothing
Complementary computing: policies for transferring callers from dialog systems to human receptionists BIBAKFull-Text 159-182
  Eric Horvitz; Tim Paek
We describe a study of the use of decision-theoretic policies for optimally joining human and automated problem-solving efforts. We focus specifically on the challenge of determining when it is best to transfer callers from an automated dialog system to human receptionists. We demonstrate the sensitivities of transfer actions to both the inferred competency of the spoken-dialog models and the current sensed load on human receptionists. The policies draw upon probabilistic models constructed via machine learning from cases that were logged by a call routing service deployed at our organization. We describe the learning of models that predict outcomes and interaction times and show how these models can be used to generate expected-utility policies that identify when it is best to transfer callers to human operators. We explore the behavior of the policies with simulations constructed from real-world call data.
Keywords: Spoken dialog systems; Machine learning; Human-machine systems; Probabilistic user modeling; Complementary computing
Discovering stages in web navigation for problem-oriented navigation support BIBAKFull-Text 183-214
  Vera Hollink; Maarte van Someren
Users of web sites often do not know exactly which information they are looking for nor what the site has to offer. The purpose of their interaction is not only to fulfill but also to articulate their information needs. In these cases users need to pass through a series of pages before they can use the information that will eventually answer their questions. Current systems that support navigation predict which pages are interesting for the users on the basis of commonalities in the contents or the usage of the pages. They do not take into account the order in which the pages must be visited. In this paper we propose a method to automatically divide the pages of a web site on the basis of user logs into sets of pages that correspond to navigation stages. The method searches for an optimal number of stages and assigns each page to a stage. The stages can be used in combination with the pages' topics to give better recommendations or to structure or adapt the site. The resulting navigation structures guide the users step by step through the site providing pages that do not only match the topic of the user's search, but also the current stage of the navigation process.
Keywords: Navigation support; Information needs; Web usage mining; Navigation stages

UMUAI 2007-07 Volume 17 Issue 3

A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation BIBAKFull-Text 217-255
  Marco Degemmis; Pasquale Lops
Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a naïve Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles.
Keywords: User modeling; Collaborative filtering; Content-based filtering; Hybrid recommenders; Machine learning; Neighborhood formation in recommender systems; WordNet
Adaptive, intelligent presentation of information for the museum visitor in PEACH BIBAKFull-Text 257-304
  Oliviero Stock; Massimo Zancanaro
The study of intelligent user interfaces and user modeling and adaptation is well suited for augmenting educational visits to museums. We have defined a novel integrated framework for museum visits and claim that such a framework is essential in such a vast domain that inherently implies complex interactivity. We found that it requires a significant investment in software and hardware infrastructure, design and implementation of intelligent interfaces, and a systematic and iterative evaluation of the design and functionality of user interfaces, involving actual visitors at every stage. We defined and built a suite of interactive and user-adaptive technologies for museum visitors, which was then evaluated at the Buonconsiglio Castle in Trento, Italy: (1) animated agents that help motivate visitors and focus their attention when necessary, (2) automatically generated, adaptive video documentaries on mobile devices, and (3) automatically generated post-visit summaries that reflect the individual interests of visitors as determined by their behavior and choices during their visit. These components are supported by underlying user modeling and inference mechanisms that allow for adaptivity and personalization. Novel software infrastructure allows for agent connectivity and fusion of multiple positioning data streams in the museum space. We conducted several experiments, focusing on various aspects of PEACH. In one, conducted with 110 visitors, we found evidence that even older users are comfortable interacting with a major component of the system.
Keywords: Adaptive mobile guides; Multimodal user interfaces; Personalized information presentation; Personal visit report
The role of human factors in stereotyping behavior and perception of digital library users: a robust clustering approach BIBAKFull-Text 305-337
  Enrique Frias-Martinez; Sherry Y. Chen
To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception.
Keywords: Digital libraries; Human factors; Stereotypes; Robust clustering; Perception; Behavior

UMUAI 2007-09 Volume 17 Issue 4

Navigation behavior models for link structure optimization BIBAKFull-Text 339-377
  Vera Hollink; Maarten van Someren
Analysis of existing methods for automatic optimization of link structures shows that these methods rely heavily on assumptions about the preferences and navigation behavior of users. Authors often do not state these assumptions explicitly and do not evaluate whether the assumptions are consistent with the actual behavior of the users of the site. This is a serious deficiency as experiments with simulated users show that incorrect assumptions can easily lead to inefficient link structures. In this work we present a framework that gives a systematic overview of alternative assumptions. On the basis of the framework we can select a set of assumptions that best matches the navigation behavior of the users in the site's log files. We also present a method for optimizing hierarchical navigation menus on the basis of the selected assumptions. This method can be used interactively under full control of a web master. The system proposes modifications of the structure and explains why these modifications lead to more efficient menus. Evaluation by means of a case study shows that the modifications that are proposed effectively reduce the expected navigation time while preserving the coherence of the menu structure.
Keywords: Link structure optimization; Web site efficiency; User behavior models; Model selection; Navigation menus
Adaptive feedback generation to support teachers in web-based distance education BIBAKFull-Text 379-413
  Essam Kosba; Vania Dimitrova; Roger Boyle
This work examines the application of user-adapted technologies to address problems experienced in web-based distance education. We have proposed an approach to support distance learning instructors by offering advice that points at problems faced by students and suggests possible activities to address these problems. The paper describes an original feedback generation framework which utilises student, group and class models derived from tracking data in web course management systems, and follows a taxonomy of feedback categories to recognise situations that are brought to the instructors' attention. The results of an empirical study in an online learning course point at benefits of the generated feedback to both instructors and students. Teachers can get a better understanding of their students by knowing what problems they may be facing, when they are behind or ahead of their peers, who can help them and how, and what roles can be assigned in discussion forums. This, in turn, can have a positive effect on students who can receive feedback tailored to their needs and problems. The evaluation study points at issues that can be related in general to planning empirical evaluations of user-adapted systems in realistic web-based learning settings.
Keywords: (Semi-)automatic advice generation; Personalised feedback; Teacher support/help; Empirical evaluation; Intelligent course management systems; Distance learning
Modeling the progression of Alzheimer's disease for cognitive assistance in smart homes BIBAKFull-Text 415-438
  Audrey Serna; Hélène Pigot; Vincent Rialle
Smart homes provide support to cognitively impaired people (such as those suffering from Alzheimer's disease) so that they can remain at home in an autonomous and safe way. Models of this impaired population should benefit the cognitive assistance's efficiency and responsiveness. This paper presents a way to model and simulate the progression of dementia of the Alzheimer's type by evaluating performance in the execution of an activity of daily living (ADL). This model satisfies three objectives: first, it models an activity of daily living; second, it simulates the progression of the dementia and the errors potentially made by people suffering from it, and, finally, it simulates the support needed by the impaired person. To develop this model, we chose the ACT-R cognitive architecture, which uses symbolic and subsymbolic representations. The simulated results of 100 people suffering from Alzheimer's disease closely resemble the results obtained by 106 people on an occupational assessment (the Kitchen Task Assessment).
Keywords: Cognitive assistance; Cognitive modeling; Error simulation; Cognitive architecture; Smart home; Alzheimer's disease

UMUAI 2007-12 Volume 17 Issue 5

Inferences, suppositions and explanatory extensions in argument interpretation BIBAKFull-Text 439-474
  Sarah George; Ingrid Zukerman
We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user's argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation -- a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system.
Keywords: Discourse interpretation; Suppositions; Explanatory extensions; Probabilistic approach; Bayesian networks
Predicting time-sharing in mobile interaction BIBAKFull-Text 475-510
  Miikka Miettinen; Antti Oulasvirta
The era of modern personal and ubiquitous computers is beset with the problem of fragmentation of the user's time between multiple tasks. Several adaptations have been envisioned that would support the performance of the user in the dynamically changing contexts in which interactions with mobile devices take place. This paper assesses the feasibility of sensor-based prediction of time-sharing, operationalized in terms of the number of glances, the duration of the longest glance, and the total and average durations of the glances to the interaction task. The data used for constructing and validating the predictive models was acquired from a field study (N=28), in which subjects performing mobile browsing tasks were observed for approximately 1 h in a variety of environments and situations. The predictive accuracy achieved in binary classification tasks was about 70% (about 20% above default), and the most informative sensors were related to the environment and interactions with the mobile device. Implications to the feasibility of different kinds of adaptations are discussed.
Keywords: Time-sharing; Attention; Multitasking; Interruptions; Mobile interaction; Mobility; Classification; Predictive models; Bayesian networks