| Modeling Emotion and Its Expression in Virtual Humans | | BIBA | Full-Text | 1-2 | |
| Stacy Marsella | |||
| A growing body of work in psychology and the neurosciences has documented the functional, often adaptive role of emotions in human behavior. This has led to a significant growth in research on computational models of human emotional processes, fueled both by their basic research potential as well as the promise that the function of emotion in human behavior can be exploited in a range of applications. Computational models transform theory construction by providing a framework for studying emotion processes that augments what is feasible in more traditional laboratory settings. Modern research in the psychological processes and neural underpinnings of emotion is also transforming the science of computation. In particular, findings on the role that emotions play in human behavior have motivated artificial intelligence and robotics research to explore whether modeling emotion processes can lead to more intelligent, flexible and capable systems. Further, as research has revealed the deep role that emotion and its expression play in human social interaction, researchers have proposed that more effective human computer interaction can be realized if the interaction is mediated both by a model of the user's emotional state as well as by the expression of emotions. | |||
| AdHeat -- An Influence-Based Diffusion Model for Propagating Hints to Personalize Social Ads | | BIBA | Full-Text | 3 | |
| Edward Y. Chang | |||
| AdHeat is our newly developed social ad model considering user influence in addition to relevance for matching ads. Traditionally, ad placement employs the relevance model. Such a model matches ads with Web page content, user interests, or both. We have observed, however, on social networks that the relevance model suffers from two shortcomings. First, influential users (users who contribute opinions) seldom click ads that are highly relevant to their expertise. Second, because influential users' contents and activities are attractive to other users, hint words summarizing their expertise and activities may be widely preferred. Therefore, we propose AdHeat, which diffuses hint words of influential users to others and then matches ads for each user with aggregated hints. Our experimental results on a large-scale social network show that AdHeat outperforms the relevance model on CTR (click through rate) by significant margins. In this talk, the algorithms employed by AdHeat and solutions to address scalability issues are presented. | |||
| Can Concept-Based User Modeling Improve Adaptive Visualization? | | BIBA | Full-Text | 4-15 | |
| Jae-wook Ahn; Peter Brusilovsky | |||
| Adaptive visualization can present user-adaptive information in such a way as to help users to analyze complicated information spaces easily and intuitively. We presented an approach called Adaptive VIBE, which extended the traditional reference point-based visualization algorithm, so that it could adaptively visualize documents of interest. The adaptive visualization was implemented by separating the effects of user models and queries within the document space and we were able to show the potential of the proposed idea. However, adaptive visualization still remained in the simple bag-of-words realm. The keywords used to construct the user models were not effective enough to express the concepts that need to be included in the user models. In this study, we tried to improve the old-fashioned keyword-only user models by adopting more concept-rich named-entities. The evaluation results show the strengths and shortcomings of using named-entities as conceptual elements for visual user models and the potential to improve the effectiveness of personalized information access systems. | |||
| Interweaving Public User Profiles on the Web | | BIBA | Full-Text | 16-27 | |
| Fabian Abel; Nicola Henze; Eelco Herder; Daniel Krause | |||
| While browsing the Web, providing profile information in social networking services, or tagging pictures, users leave a plethora of traces. In this paper, we analyze the nature of these traces. We investigate how user data is distributed across different Web systems, and examine ways to aggregate user profile information. Our analyses focus on both explicitly provided profile information (name, homepage, etc.) and activity data (tags assigned to bookmarks or images). The experiments reveal significant benefits of interweaving profile information: more complete profiles, advanced FOAF/vCard profile generation, disclosure of new facets about users, higher level of self-information induced by the profiles, and higher precision for predicting tag-based profiles to solve the cold start problem. | |||
| Modeling Long-Term Search Engine Usage | | BIBAK | Full-Text | 28-39 | |
| Ryen W. White; Ashish Kapoor; Susan T. Dumais | |||
| Search engines are key components in the online world and the choice of
search engine is an important determinant of the user experience. In this work
we seek to model user behaviors and determine key variables that affect search
engine usage. In particular, we study the engine usage behavior of more than
ten thousand users over a period of six months and use machine learning
techniques to identify key trends in the usage of search engines and their
relationship with user satisfaction. We also explore methods to determine
indicators that are predictive of user trends and show that accurate predictive
user models of search engine usage can be developed. Our findings have
implications for users as well as search engine designers and marketers seeking
to better understand and retain their users. Keywords: Search Engine; Predictive Model | |||
| Analysis of Strategies for Building Group Profiles | | BIBAK | Full-Text | 40-51 | |
| Christophe Senot; Dimitre Kostadinov; Makram Bouzid; Jérôme Picault; Armen Aghasaryan; Cédric Bernier | |||
| Today most of existing personalization systems (e.g. content recommenders,
or targeted ad) focus on individual users and ignore the social situation in
which the services are consumed. However, many human activities are social and
involve several individuals whose tastes and expectations must be taken into
account by the service providers. When a group profile is not available,
different profile aggregation strategies can be applied to recommend adequate
content and services to a group of users based on their individual profiles. In
this paper, we consider an approach intended to determine the factors that
influence the choice of an aggregation strategy. We present a preliminary
evaluation made on a real large-scale dataset of TV viewings, showing how group
interests can be predicted by combining individual user profiles through an
appropriate strategy. The conducted experiments compare the group profiles
obtained by aggregating individual user profiles according to various
strategies to the "reference" group profile obtained by directly analyzing
group consumptions. Keywords: group recommendations; individual profile; group profiles; aggregation
strategies; evaluation | |||
| Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor | | BIBAK | Full-Text | 52-63 | |
| Ryan S. J. d. Baker; Albert T. Corbett; Sujith M. Gowda; Angela Z. Wagner; Benjamin A. MacLaren; Linda R. Kauffman; Aaron P. Mitchell; Stephen Giguere | |||
| Intelligent tutoring systems that utilize Bayesian Knowledge Tracing have
achieved the ability to accurately predict student performance not only within
the intelligent tutoring system, but on paper post-tests outside of the system.
Recent work has suggested that contextual estimation of student guessing and
slipping leads to better prediction within the tutoring software (Baker,
Corbett, & Aleven, 2008a, 2008b). However, it is not yet clear whether this
new variant on knowledge tracing is effective at predicting the latent student
knowledge that leads to successful post-test performance. In this paper, we
compare the Contextual-Guess-and-Slip variant on Bayesian Knowledge Tracing to
classical four-parameter Bayesian Knowledge Tracing and the Individual
Difference Weights variant of Bayesian Knowledge Tracing (Corbett &
Anderson, 1995), investigating how well each model variant predicts post-test
performance. We also test other ways to utilize contextual estimation of
slipping within the tutor in post-test prediction, and discuss hypotheses for
why slipping during tutor use is a significant predictor of post-test
performance, even after Bayesian Knowledge Tracing estimates are controlled
for. Keywords: Student Modeling; Bayesian Knowledge Tracing; Intelligent Tutoring Systems;
Educational Data Mining; Contextual Slip | |||
| Working Memory Span and E-Learning: The Effect of Personalization Techniques on Learners' Performance | | BIBAK | Full-Text | 64-74 | |
| Nikos Tsianos; Panagiotis Germanakos; Zacharias Lekkas; Costas Mourlas; George Samaras | |||
| This research paper presents the positive effect of incorporating
individuals' working memory (WM) span as a personalization factor in terms of
improving users' academic performance in the context of adaptive educational
hypermedia. The psychological construct of WM is robustly related to
information processing and learning, while there is a wide differentiation of
WM span among individuals. Hence, in an effort to examine the role of cognitive
and affective factors in adaptive hypermedia along with psychometric user
profiling considerations, WM has a central role in the authors' effort to
develop a user information processing model. Encouraged by previous findings, a
larger scale study has been conducted with the participation of 230 university
students in order to elucidate if it is possible through personalization to
increase the performance of learners with lower levels of WM span. According to
the results, users with low WM performed better in the personalized condition,
which involved segmentation of the web content and aesthetical annotation,
while users with medium/high WM span were slightly negatively affected by the
same techniques. Therefore, it can by supported it is possible to specifically
address the problem of low WM span with significant results. Keywords: Adaptive Hypermedia; Working Memory; User Profiling; Cognitive Psychology;
Individual Differences | |||
| Scaffolding Self-directed Learning with Personalized Learning Goal Recommendations | | BIBAK | Full-Text | 75-86 | |
| Tobias Ley; Barbara Kump; Cornelia Gerdenitsch | |||
| Adaptive scaffolding has been proposed as an efficient means for supporting
self-directed learning both in educational as well as in adaptive learning
systems research. However, the effects of adaptation on self-directed learning
and the differential contributions of different adaptation models have not been
systematically examined. In this paper, we examine whether personalized
scaffolding in the learning process improves learning. We conducted a
controlled lab study in which 29 students had to solve several tasks and learn
with the help of an adaptive learning system in a within-subjects control
condition design. In the learning process, participants obtained
recommendations for learning goals from the system in three conditions: fixed
scaffolding where learning goals were generated from the domain model,
personalized scaffolding where these recommendations were ranked according to
the user model, and random suggestions of learning goals (control condition).
Students in the two experimental conditions clearly outperformed students in
the control condition and felt better supported by the system. Additionally,
students who received personalized scaffolding selected fewer learning goals
than participants from the other groups. Keywords: Adaptive scaffolding; Personalization; Adaptive Learning Systems;
Self-directed learning; Layered Evaluation; APOSDLE | |||
| Instructional Video Content Employing User Behavior Analysis: Time Dependent Annotation with Levels of Detail | | BIBAK | Full-Text | 87-98 | |
| Junzo Kamahara; Takashi Nagamatsu; Masashi Tada; Yohei Kaieda; Yutaka Ishii | |||
| We develop a multimedia instruction system for the inheritance of skills.
This system identifies the difficult segments of video by analyzing user
behavior. Difficulties may be inferred by the learner's requiring more time to
fully process a portion of video; they may replay or pause the video during the
course of a segment, or play it at a slow speed. These difficult video segments
are subsequently assumed to require the addition of expert, instructor
annotations, in order to enable learning. We propose a time-dependent
annotation mechanism, employing a level of detail (LoD) approach. This
annotation is superimposed upon the video, based on the user's selected speed
of playback. The LoD, which reflects the difficulty of the training material,
is used to adapt whether to display the annotation to the user. We present the
results of an experiment that describes the relationship between the difficulty
of material and the LoDs. Keywords: User Behavior; Level of Detail; Timed Annotation | |||
| A User-and Item-Aware Weighting Scheme for Combining Predictive User Models | | BIBA | Full-Text | 99-110 | |
| Fabian Bohnert; Ingrid Zukerman | |||
| Hybridising user models can improve predictive accuracy. However, research on linearly combining predictive user models (e.g., used in recommender systems) has often made the implicit assumption that the individual models perform uniformly across the user and item space, using static model weights when computing a weighted average of the predictions of the individual models. This paper proposes a weighting scheme which combines user- and item-specific weight vectors to compute user- and item-aware model weights. The proposed hybridisation approach adaptively estimates online the model parameters that are specific to a target user as information about this user becomes available. Hence, it is particularly well-suited for domains where little or no information regarding the target user's preferences or interests is available at the time of offline model training. The proposed weighting scheme is evaluated by applying it to a real-world scenario from the museum domain. Our results show that in our domain, our hybridisation approach attains a higher predictive accuracy than the individual component models. Additionally, our approach outperforms a non-adaptive hybrid model that uses static model weights. | |||
| PersonisJ: Mobile, Client-Side User Modelling | | BIBA | Full-Text | 111-122 | |
| Simon Gerber; Michael Fry; Judy Kay; Bob Kummerfeld; Glen Pink; Rainer Wasinger | |||
| The increasing trend towards powerful mobile phones opens many possibilities for valuable personalised services to be available on the phone. Client-side personalisation for these services has important benefits when connectivity to the cloud is restricted or unavailable. The user may also find it desirable when they prefer that their user model be kept only on their phone and under their own control, rather than under the control of the cloud-based service provider. This paper describes PersonisJ, a user modelling framework that can support client-side personalisation on the Android phone platform. We discuss the particular challenges in creating a user modelling framework for this platform. We have evaluated PersonisJ at two levels: we have created a demonstrator application that delivers a personalised museum tour based on client-side personalisation; we also report on evaluations of its scalability. Contributions of this paper are the description of the architecture, the implementation, and the evaluation of a user modelling framework for client-side personalisation on mobile phones. | |||
| Twitter, Sensors and UI: Robust Context Modeling for Interruption Management | | BIBA | Full-Text | 123-134 | |
| Justin Tang; Donald J. Patterson | |||
| In this paper, we present the results of a two-month field study of fifteen people using a software tool designed to model changes in a user's availability. The software uses status update messages, as well as sensors, to detect changes in context. When changes are identified using the Kullback-Leibler Divergence metric, users are prompted to broadcast their current context to their social networks. The user interface method by which the alert is delivered is evaluated in order to minimize the impact on the user's workflow. By carefully coupling both algorithms and user interfaces, interruptions made by the software tool can be made valuable to the user. | |||
| Ranking Feature Sets for Emotion Models Used in Classroom Based Intelligent Tutoring Systems | | BIBA | Full-Text | 135-146 | |
| David G. Cooper; Kasia Muldner; Ivon Arroyo; Beverly Park Woolf; Winslow Burleson | |||
| Recent progress has been made by using sensors with Intelligent Tutoring Systems in classrooms in order to predict the affective state of students users. If tutors are able to interpret sensor data with new students based on past experience, rather than having to be individually trained, then this will enable tutor developers to evaluate various methods of adapting to each student's affective state using consistent predictions. In the past, our classifiers have predicted student emotions with an accuracy between 78% and 87%. However, it is still unclear which sensors are best, and the educational technology community needs to know this to develop better than baseline classifiers, e.g. ones that use only frequency of emotional occurrence to predict affective state. This paper suggests a method to clarify classifier ranking for the purpose of affective models. The method begins with a careful collection of a training and testing set, each from a separate population, and concludes with a non-parametric ranking of the trained classifiers on the testing set. We illustrate this method with classifiers trained on data collected in the Fall of 2008 and tested on data collected in the Spring of 2009. Our results show that the classifiers for some affective states are significantly better than the baseline model; a validation analysis showed that some but not all classifier rankings generalize to new settings. Overall, our analysis suggests that though there is some benefit gained from simple linear classifiers, more advanced methods or better features may be needed for better classification performance. | |||
| Inducing Effective Pedagogical Strategies Using Learning Context Features | | BIBA | Full-Text | 147-158 | |
| Min Chi; Kurt VanLehn; Diane Litman; Pamela Jordan | |||
| Effective pedagogical strategies are important for e-learning environments. While it is assumed that an effective learning environment should craft and adapt its actions to the user's needs, it is often not clear how to do so. In this paper, we used a Natural Language Tutoring System named Cordillera and applied Reinforcement Learning (RL) to induce pedagogical strategies directly from pre-existing human user interaction corpora. 50 features were explored to model the learning context. Of these features, domain-oriented and system performance features were the most influential while user performance and background features were rarely selected. The induced pedagogical strategies were then evaluated on real users and results were compared with pre-existing human user interaction corpora. Overall, our results show that RL is a feasible approach to induce effective, adaptive pedagogical strategies by using a relatively small training corpus. Moreover, we believe that our approach can be used to develop other adaptive and personalized learning environments. | |||
| "Yes!": Using Tutor and Sensor Data to Predict Moments of Delight during Instructional Activities | | BIBAK | Full-Text | 159-170 | |
| Kasia Muldner; Winslow Burleson; Kurt VanLehn | |||
| A long standing challenge for intelligent tutoring system (ITS) designers
and educators alike is how to encourage students to take pleasure and interest
in learning activities. In this paper, we present findings from a user study
involving students interacting with an ITS, focusing on when students express
excitement, what we dub "yes!" moments. These findings include an
empirically-based user model that relies on both interaction and physiological
sensor features to predict "yes!" events; here we describe this model, its
validation, and initial indicators of its importance for understanding and
fostering student interest. Keywords: interest; motivation; empirically-based model; sensing devices | |||
| A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile | | BIBA | Full-Text | 171-182 | |
| Mariam Daoud; Lynda Tamine; Mohand Boughanem | |||
| The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs. We present in this paper a personalized document ranking model based on an extended graph-based distance measure that exploits a semantic user profile derived from a predefined web ontology (ODP). The measure is based on combining Minimum Common Supergraph (MCS) and Maximum Common Subgraph (mcs) between graphs representing respectively the document and the user profile. We extend this measure in order to take into account a semantic recovery between the document and the user profile through common concepts and cross links connecting the two graphs. Results show the effectiveness of our personalized graph-based ranking model compared to Yahoo search results. | |||
| Interaction and Personalization of Criteria in Recommender Systems | | BIBAK | Full-Text | 183-194 | |
| Shawn R. Wolfe; Yi Zhang | |||
| A user's informational need and preferences can be modeled by criteria,
which in turn can be used to prioritize candidate results and produce a ranked
list. We examine the use of such a criteria-based user model separately in two
representative recommendation tasks: news article recommendations and product
recommendations. We ask the following: are there nonlinear interactions among
the criteria; and should the models be personalized? We assume that that user
ratings on each criterion are available, and use machine learning to infer a
user model that combines these multiple ratings into a single overall rating.
We found that the ratings of different criteria have a nonlinear interaction in
some cases, for example, article novelty and subject relevance often interact.
We also found that these interactions vary from user to user. Keywords: information filtering; multiple criteria; nonlinear models | |||
| Collaborative Inference of Sentiments from Texts | | BIBA | Full-Text | 195-206 | |
| Yanir Seroussi; Ingrid Zukerman; Fabian Bohnert | |||
| Sentiment analysis deals with inferring people's sentiments and opinions from texts. An important aspect of sentiment analysis is polarity classification, which consists of inferring a document's polarity -- the overall sentiment conveyed by the text -- in the form of a numerical rating. In contrast to existing approaches to polarity classification, we propose to take the authors of the documents into account. Specifically, we present a nearest-neighbour collaborative approach that utilises novel models of user similarity. Our evaluation shows that our approach improves on state-of-the-art performance, and yields insights regarding datasets for which such an improvement is achievable. | |||
| User Modelling for Exclusion and Anomaly Detection: A Behavioural Intrusion Detection System | | BIBAK | Full-Text | 207-218 | |
| Grant Pannell; Helen Ashman | |||
| User models are generally created to personalise information or share user
experiences among like-minded individuals. An individual's characteristics are
compared to those of some canonical user type, and the user included in various
user groups accordingly. Those user groups might be defined according to
academic ability or recreational interests, but the aim is to include the user
in relevant groups where appropriate. The user model described here operates on
the principle of exclusion, not inclusion, and its purpose is to detect
atypical behaviour, seeing if a user falls outside a category, rather than
inside one. That is, it performs anomaly detection against either an individual
user model or a typical user model. Such a principle can be usefully applied in
many ways, such as early detection of illness, or discovering students with
learning issues. In this paper, we apply the anomaly detection principle to the
detection of intruders on a computer system masquerading as real users, by
comparing the behaviour of the intruder with the expected behaviour of the user
as characterised by their user model. This behaviour is captured in
characteristics such as typing habits, Web page usage and application usage. An
experimental intrusion detection system (IDS) was built with user models
reflecting these characteristics, and it was found that comparison with a small
number of key characteristics from a user model can very quickly detect
anomalies and thus identify an intruder. Keywords: user model; exclusion; anomaly detection; behavioural IDS | |||
| IntrospectiveViews: An Interface for Scrutinizing Semantic User Models | | BIBA | Full-Text | 219-230 | |
| Fedor Bakalov; Birgitta König-Ries; Andreas Nauerz; Martin Welsch | |||
| User models are a key component for user-adaptive systems. They represent information about users such as interests, expertise, goals, traits, etc. This information is used to achieve various adaptation effects, e.g., recommending relevant documents or products. To ensure acceptance by users, these models need to be scrutable, i.e., users must be able to view and alter them to understand and if necessary correct the assumptions the system makes about the user. However, in most existing systems, this goal is not met. In this paper, we introduce IntrospectiveViews, an interface that enables the user to view and edit her user model. Furthermore, we present the results of a formative evaluation that show the importance users give in general to different aspects of scrutable user models and also substantiate our claim that IntrospectiveViews is an appropriate realization of an interface to such models. | |||
| Analyzing Community Knowledge Sharing Behavior | | BIBAK | Full-Text | 231-242 | |
| Styliani Kleanthous; Vania Dimitrova | |||
| The effectiveness of personalized support provided to virtual communities
depends on what we know about a particular community and in which areas the
community may need support. Following organizational psychology theories, we
have developed algorithms to automatically detect patterns of knowledge sharing
in a closely-knit virtual community, focusing on transactive memory, shared
mental models, and cognitive centrality. The automatic detection of problematic
areas enables taking decisions about notifications targeted at different
community members but aiming at improving the functioning of the community as a
whole. The paper presents graph-based algorithms for detecting community
knowledge sharing patterns, and illustrates, based on a study with an existing
community, how these patterns can be used for community-tailored support. Keywords: Community Knowledge Sharing; Closely-knit Communities; Graph Mining for
Community Modelling | |||
| A Data-Driven Technique for Misconception Elicitation | | BIBAK | Full-Text | 243-254 | |
| Eduardo Guzmán; Ricardo Conejo; Jaime Gálvez | |||
| When a quantitative student model is constructed, one of the first tasks to
perform is to identify the domain concepts assessed. In general, this task is
easily done by the domain experts. In addition, the model may include some
misconceptions which are also identified by these experts. Identifying these
misconceptions is a difficult task, however, and one which requires
considerable previous experience with the students. In fact, sometimes it is
difficult to relate these misconceptions to the elements in the knowledge
diagnostic system which feeds the student model. In this paper we present a
data-driven technique which aims to help elicit the domain misconceptions. It
also aims to relate these misconceptions with the assessment activities (e.g.
exercises, problems or test questions), which assess the subject in question. Keywords: Student Modeling; Misconception Elicitation; Student Knowledge Diagnosis | |||
| Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing | | BIBAK | Full-Text | 255-266 | |
| Zachary A. Pardos; Neil T. Heffernan | |||
| The field of intelligent tutoring systems has been using the well known
knowledge tracing model, popularized by Corbett and Anderson (1995), to track
student knowledge for over a decade. Surprisingly, models currently in use do
not allow for individual learning rates nor individualized estimates of student
initial knowledge. Corbett and Anderson, in their original articles, were
interested in trying to add individualization to their model which they
accomplished but with mixed results. Since their original work, the field has
not made significant progress towards individualization of knowledge tracing
models in fitting data. In this work, we introduce an elegant way of
formulating the individualization problem entirely within a Bayesian networks
framework that fits individualized as well as skill specific parameters
simultaneously, in a single step. With this new individualization technique we
are able to show a reliable improvement in prediction of real world data by
individualizing the initial knowledge parameter. We explore three difference
strategies for setting the initial individualized knowledge parameters and
report that the best strategy is one in which information from multiple skills
is used to inform each student's prior. Using this strategy we achieved lower
prediction error in 33 of the 42 problem sets evaluated. The implication of
this work is the ability to enhance existing intelligent tutoring systems to
more accurately estimate when a student has reached mastery of a skill.
Adaptation of instruction based on individualized knowledge and learning speed
is discussed as well as open research questions facing those that wish to
exploit student and skill information in their user models. Keywords: Knowledge Tracing; Individualization; Bayesian Networks; Data Mining;
Prediction; Intelligent Tutoring Systems | |||
| Detecting Gaming the System in Constraint-Based Tutors | | BIBAK | Full-Text | 267-278 | |
| Ryan S. J. d. Baker; Antonija Mitrovic; Moffat Mathews | |||
| Recently, detectors of gaming the system have been developed for several
intelligent tutoring systems where the problem-solving process is reified, and
gaming consists of systematic guessing and help abuse. Constraint-based tutors
differ from the tutors where gaming detectors have previously been developed on
several dimensions: in particular, higher-level answers are assessed according
to a larger number of finer-grained constraints, and feedback is split into
levels rather than an entire help sequence being available at any time.
Correspondingly, help abuse behaviors differ, including behaviors such as
rapidly repeating the same answer or blank answers to elicit answers. We use
text replay labeling in combination with educational data mining methods to
create a gaming detector for SQL-Tutor, a popular constraint-based tutor. This
detector assesses gaming at the level of multiple-submission sequences and is
accurate both at identifying gaming within submission sequences and at
identifying how much each student games the system. It achieves only limited
success, however, at distinguishing different types of gaming behavior from
each other. Keywords: gaming the system; educational data mining; machine learning | |||
| Bayesian Credibility Modeling for Personalized Recommendation in Participatory Media | | BIBA | Full-Text | 279-290 | |
| Aaditeshwar Seth; Jie Zhang; Robin Cohen | |||
| In this paper, we focus on the challenge that users face in processing messages on the web posted in participatory media settings, such as blogs. It is desirable to recommend to users a restricted set of messages that may be most valuable to them. Credibility of a message is an important criteria to judge its value. In our approach, theories developed in sociology, political science and information science are used to design a model for evaluating the credibility of messages that is user-specific and that is sensitive to the social network in which the user resides. To recommend new messages to users, we employ Bayesian learning, built on past user behaviour, integrating new concepts of context and completeness of messages inspired from the strength of weak ties hypothesis, from social network theory. We are able to demonstrate that our method is effective in providing the most credible messages to users and significantly enhances the performance of collaborative filtering recommendation, through a user study on the digg.com dataset. | |||
| A Study on User Perception of Personality-Based Recommender Systems | | BIBAK | Full-Text | 291-302 | |
| Rong Hu; Pearl Pu | |||
| Our previous research indicates that using personality quizzes is a viable
and promising way to build user profiles to recommend entertainment products.
Based on these findings, our current research further investigates the
feasibility of using personality quizzes to build user profiles not only for an
active user but also his or her friends. We first propose a general method that
infers users' music preferences in terms of their personalities. Our in-depth
user studies show that while active users perceive the recommended items to be
more accurate for their friends, they enjoy more using personality quiz based
recommenders for finding items for themselves. Additionally, we explore if
domain knowledge has an influence on users' perception of the system. We found
that novice users, who are less knowledgeable about music, generally
appreciated more personality-based recommenders. Finally, we propose some
design issues for recommender systems using personality quizzes. Keywords: Recommender System; Personality; Domain Knowledge; User Study; User Modeling | |||
| Compass to Locate the User Model I Need: Building the Bridge between Researchers and Practitioners in User Modeling | | BIBAK | Full-Text | 303-314 | |
| Armelle Brun; Anne Boyer; Liana Razmerita | |||
| User modeling is a complex task, and many user modeling techniques are
proposed in the existing literature, but the way these models are presented is
not homogeneous, the domain is fragmented and these models are not directly
comparable. Thus there is a need for a unified view of the whole user modeling
domain and of the applicability of the models to specific applications,
contexts or according to specific requirements, type of data, availability of
data, etc. A common question companies may ask when they want to build and
exploit a user model in order to implement different kinds of personalization
or adaptive systems is: "Given my specific requirements, which user modeling
technique can be used?". No obvious answer can be given to this question. This
article aims to propose a topic map of user modeling in connection with input
data, data types, accessibility, approach, specific requirements and users'
data acquisition methods. This schema/topic map is aimed to help practitioners
and researchers as well to answer the above mentioned question. Furthermore the
article provides two concrete scenarios in the area of recommender systems and
shows how the topic map may be used for these scenarios and real world
applications. Keywords: user model; user modeling; recommender systems; personalization | |||
| myCOMAND Automotive User Interface: Personalized Interaction with Multimedia Content Based on Fuzzy Preference Modeling | | BIBAK | Full-Text | 315-326 | |
| Philipp Fischer; Andreas Nürnberger | |||
| myCOMAND case study explores the vision of an interactive user interface
(UI) in the vehicle providing access to a large variety of information items
aggregated from Web services. It was created for gaining insights into
applicability of personalization and recommendation approaches for the visual
ranking and grouping of items, composed as interactive UI layout components
(e.g. carousels, lists). Quick access to preferred and important items can
support less distracting interaction with a large web-based content collections
and smaller screen size. Content gets aggregated on the server and then
synchronized to an onboard module. Ranking for each data item is annotated
based on a user profiles with a fuzzy preferences and a shared taxonomy on
content categories. Preference values are implicitly learned from user
interaction, but can be set explicitly by the user too. A circular UI component
for browsing Internet radio stations is described, which dynamically groups
items into categories during scrolling. Items are ranked according to the
user's preferences and item novelty. A visual overview mode helps to quickly
review the structure of large content collections. Keywords: Fuzzy Preference Modelling; Content-based Recommendation; Graphical User
Interfaces; Haptic I/O; Prototyping; User Interface Framework Patterns;
Automotive Human Machine Interaction | |||
| User Modeling for Telecommunication Applications: Experiences and Practical Implications | | BIBA | Full-Text | 327-338 | |
| Heath Hohwald; Enrique Frías-Martínez; Nuria Oliver | |||
| Telecommunication applications based on user modeling focus on extracting customer behavior and preferences from the information implicitly included in Call Detail Record (CDR) datasets. Even though there are many different application areas (fraud detection, viral and targeted marketing, churn prediction, etc.) they all share a common data source (CDRs) and a common set of features for modeling the user. In this paper we present our experience with different applications areas in generating user models from massive real datasets of both mobile phone and landline subscriber activity. We present the analysis of a dataset containing the traces of 50,000 mobile phone users and 50,000 landline users from the same geographical area for a period of six months and compare the different behaviors when using landlines and mobile phones and the implications that such differences have for each application. Our results indicate that user models for a variety of applications can be generated efficiently and in a homogeneous way using an architecture based on distributed computing and that there are numerous differences between mobile phone and landline users that have relevant practical implications. | |||
| Mobile Web Profiling: A Study of Off-Portal Surfing Habits of Mobile Users | | BIBA | Full-Text | 339-350 | |
| Daniel Olmedilla; Enrique Frías-Martínez; Rubén Lara | |||
| The World Wide Web has provided users with the opportunity to access from any computer the largest set of information ever existing. Researchers have analyzed how such users surf the Web, and such analysis has been used to improve existing services (e.g., by means of data mining and personalization techniques) as well as the generation of new ones (e.g., online targeted advertisement). In recent years, a new trend has developed by which users do not need a computer to access the Web. Instead, the low prices of mobile data connections allow them to access it anywhere anytime. Some studies analyze how users access the Web on their handsets, but these studies use only navigation logs from a specific portal. Therefore, very little attention (due to the complexity of obtaining the data) has been given to how users surf the Web (off-portal) from their mobiles and how that information could be used to build user profiles. This paper analyzes full navigation logs of a large set of mobile users in a developed country, providing useful information about the way those users access the Web. Additionally, it explores how navigation logs can be categorized, and thus user's interest can be modeled, by using online sources of information such as Web directories and social tagging systems. | |||
| Personalized Implicit Learning in a Music Recommender System | | BIBAK | Full-Text | 351-362 | |
| Suzana Kordumova; Ivana Kostadinovska; Mauro Barbieri; Verus Pronk; Jan Korst | |||
| Recommender systems typically require feedback from the user to learn the
user's taste. This feedback can come in two forms: explicit and implicit.
Explicit feedback consists of ratings provided by the user for a number of
items, while implicit feedback comes from observing user actions on items.
These actions have to be interpreted by the recommender system and translated
into a rating. In this paper we propose a method to learn how to translate user
actions on items to ratings on these items by correlating user actions with
explicit feedback. We do this by associating user actions to rated items and
subsequently applying naive Bayesian classification to rate new items with
which the user has interacted. We apply and evaluate our method on data from a
web-based music service and we show its potential as an addition to explicit
rating. Keywords: implicit learning; recommender systems; user behavior; relief algorithm;
naive Bayesian classification | |||
| Personalised Pathway Prediction | | BIBA | Full-Text | 363-368 | |
| Fabian Bohnert; Ingrid Zukerman | |||
| This paper proposes a personalised frequency-based model for predicting a user's pathway through a physical space, based on non-intrusive observations of users' previous movements. Specifically, our approach estimates a user's transition probabilities between discrete locations utilising personalised transition frequency counts, which in turn are estimated from the movements of other similar users. Our evaluation with a real-world dataset from the museum domain shows that our approach performs at least as well as a non-personalised frequency-based baseline, while attaining a higher predictive accuracy than a model based on the spatial layout of the physical museum space. | |||
| Towards a Customization of Rating Scales in Adaptive Systems | | BIBA | Full-Text | 369-374 | |
| Federica Cena; Fabiana Vernero; Cristina Gena | |||
| In web-based adaptive systems, the same rating scales are usually provided to all users for expressing their preferences with respect to various items. It emerged from a user experiment that we recently carried out that different users show different preferences with respect to the rating scales to use in the interface of adaptive systems, given the particular topic they are evaluating. Starting from this finding, we propose to allow users to choose the kind of rating scale they prefer. This approach raises various issues; the most important is that of how an adaptation algorithm can properly deal with values coming from heterogeneous rating scales. We conducted an experiment to investigate how users rate the same object on different rating scales. On the basis of our interpretation of these results, as an example of one possible solution approach, we propose a three-phase normalization process for mapping preferences expressed with different rating scales onto a unique system representation. | |||
| Eye-Tracking Study of User Behavior in Recommender Interfaces | | BIBAK | Full-Text | 375-380 | |
| Li Chen; Pearl Pu | |||
| Recommender systems, as a type of Web personalized service to support users'
online product searching, have been widely developed in recent years but with
primary emphasis on algorithm accuracy. In this paper, we particularly
investigate the efficacy of recommender interface designs in affecting users'
decision making strategies through the observation of their eye movements and
product selection behavior. One interface design is the standard list interface
where all recommended items are listed one by one. Another two are layout
variations of organization-based interface where recommendations are grouped
into categories. The eye-tracking user evaluation shows that the organization
interfaces, especially the one with a quadrant layout, can significantly
attract users' attentions to more items, with the resulting benefit to enhance
their objective decision quality. Keywords: recommender systems; list interface; organization design; eye-tracking
study; users' adaptive behavior | |||
| Recommending Food: Reasoning on Recipes and Ingredients | | BIBAK | Full-Text | 381-386 | |
| Jill Freyne; Shlomo Berkovsky | |||
| With the number of people considered to be obese rising across the globe,
the role of IT solutions in health management has been receiving increased
attention by medical professionals in recent years. This paper focuses on an
initial step toward understanding the applicability of recommender techniques
in the food and diet domain. By understanding the food preferences and
assisting users to plan a healthy and appealing meal, we aim to reduce the
effort required of users to change their diet. As an initial feasibility study,
we evaluate the performance of collaborative filtering, content-based and
hybrid recommender algorithms on a dataset of 43,000 ratings from 512 users. We
report on the accuracy and coverage of the algorithms and show that a
content-based approach with a simple mechanism that breaks down recipe ratings
into ingredient ratings performs best overall. Keywords: Collaborative filtering; content-based; ingredient; recipes | |||
| Disambiguating Search by Leveraging a Social Context Based on the Stream of User's Activity | | BIBA | Full-Text | 387-392 | |
| Tomás Kramár; Michal Barla; Mária Bieliková | |||
| Older studies have proved that when searching information on the Web, users tend to write short queries, unconsciously trying to minimize the cognitive load. However, as these short queries are very ambiguous, search engines tend to find the most popular meaning -- someone who does not know anything about cascading stylesheets might search for a music band called css and be very surprised about the results. In this paper we propose a method which can infer additional keywords for a search query by leveraging a social network context and a method to build this network from the stream of user's activity on the Web. | |||
| Features of an Independent Open Learner Model Influencing Uptake by University Students | | BIBAK | Full-Text | 393-398 | |
| Susan Bull | |||
| Building on previous research with an independent open learner model in a
range of university courses, this paper investigates features that may
influence student choice about whether to use the environment in a particular
course. It was found that some features are considered particularly important
by students, but other features are less influential in students' decisions to
use an independent open learner model. Recommendations for features to consider
promoting uptake of this type of environment are given. Keywords: Open learner model; learner preferences; adaptive e-learning | |||
| Recognizing and Predicting the Impact on Human Emotion (Affect) Using Computing Systems | | BIBAK | Full-Text | 399-402 | |
| David G. Cooper | |||
| Emotional intelligence is a clear factor in education [1-3], health care
[4], and day to day interaction. With the increasing use of computer
technology, computers are interacting with more and more individuals. This
interaction provides an opportunity to increase knowledge about human emotion
for human consumption, well-being, and improved computer adaptation.
This research makes five main contributions. 1) Construct a method for determining a set of sensor features that can be automatically processed to predict human emotional changes in observed people. 2) Identify principles, algorithms, and classifiers that enable computational recognition of human emotion. 3) Apply this method to an intelligent tutoring system instrumented with sensors. 4) Apply and adapt the method to audio and video sensors for a number of applications such as a) detection of psychological disorders, b) detection of emotional changes in health care providers, c) detection of emotional impact of one person on another during video chat, and/or d) detection of emotional impact of one fictional character on another in a motion picture. 5) Integrate emotional detection technologies so that they can be used in more realistic settings. Keywords: emotional interaction; multi-sensor affective processing; smart
environments; actionable affect; social signal processing | |||
| Utilising User Texts to Improve Recommendations | | BIBA | Full-Text | 403-406 | |
| Yanir Seroussi | |||
| Recommender systems traditionally rely on numeric ratings to represent user opinions, and thus are limited by the single-dimensional nature of such ratings. Recent years have seen an abundance of user-generated texts available online, and advances in natural language processing allow us to better understand users by analysing the texts they write. Specifically, sentiment analysis enables inference of people's sentiments and opinions from texts, while authorship attribution investigates authors' characteristics. We propose to use these techniques to build text-based user models, and incorporate these models into state-of-the-art recommender systems to generate recommendations that are based on a more profound understanding of the users than rating-based recommendations. Our preliminary results suggest that this is a promising direction. | |||
| Semantically-Enhanced Ubiquitous User Modeling | | BIBA | Full-Text | 407-410 | |
| Till Plumbaum | |||
| Semantically-enhanced Ubiquitous User Modeling aims at the management of distributed user models and the integration into ontologies to share user information amongst adaptive applications for personalization purposes. To reach this goal, different problems have to be solved. The collection of implicit user information by observing the user behavior on dynamic web applications is important to better understand the user interests and needs. The aggregation of different user models is essential to combine all available user information to one big knowledge repository. Additionally, the Semantic Web offers new possibilities to enhance the knowledge about the user for better personalization. | |||
| User Modeling Based on Emergent Domain Semantics | | BIBAK | Full-Text | 411-414 | |
| Marián Simko; Mária Bieliková | |||
| In this paper we present an approach to user modeling based on the domain
model that we generate automatically by resource (text) content processing and
analysis of associated tags from a social annotation service. User's interests
are modeled by overlaying the domain model -- via keywords extracted from
resource's (text) content, and tags assigned by the user or other (similar)
users. The user model is derived automatically. We combine content- and
tag-based approaches, shifting our approach beyond flat "folksonomical"
representation of user interests to involve relationships between both keywords
and tags. Keywords: user modeling; emergent domain semantics; automatic domain model
composition; folksonomy; text mining | |||
| "Biographic spaces": A Personalized Smoking Cessation Intervention in Second Life | | BIBAK | Full-Text | 415-418 | |
| Ana Boa-Ventura; Luís Saboga-Nunes | |||
| In this paper we are proposing a proof-of-concept leveraging the use of 3D
virtual worlds in addictive behavior interventions. We propose a model that we
call biographic space, which embeds the successive stages that a smoker may go
through while attempting to quit smoking including emotionally loaded aspects
such as deciding to quit and post cessation withdrawal. The design of this
space is informed by storytelling and explores the rich media affordance of
virtual environments. Keywords: smoking cessation; storytelling; virtual worlds; Second Life | |||
| Task-Based User Modelling for Knowledge Work Support | | BIBAK | Full-Text | 419-422 | |
| Charlie Abela; Chris Staff; Siegfried Handschuh | |||
| A Knowledge Worker (KW) uses her computer to perform different tasks for
which she gathers and uses information from disparate sources such as the Web
and e-mail, and creates new information such as calendar events, e-mails, and
documents (resources). This forms a Task Space (TS): an information space
composed of all computer-based resources the KW uses in relation to a task.
Furthermore, KWs may switch between multiple tasks, some of which may be
suspended and resumed after some time. These effects compound the KW's ability
to organise and visualise an accurate mental model of the individual TSs. We
propose a Task-Based User Model (TBUM) that acts as the KW's mental model for
each task by automatically tracking, relating and organising resources
associated with that task. The generated TBUM can be used to support complex
activities such as task-resumption, searching within a task-context, task
sharing and collaboration. Keywords: Task-Based Computing; Personal Knowledge Management; Task-Based User Model | |||
| Enhancing User Interaction in Virtual Environments through Adaptive Personalized 3D Interaction Techniques | | BIBAK | Full-Text | 423-426 | |
| Johanna Renny Octavia; Karin Coninx; Chris Raymaekers | |||
| Leveraging interactive systems by integrating adaptivity is considered as an
important key to accommodate user diversity and enhance user interaction. A
virtual environment is a highly interactive system which involves users
performing complex tasks using diverse 3D interaction techniques. Adaptivity
has not been investigated thoroughly in the context of virtual environments.
This PhD research is concerned with embedding intelligence to enhance user
interaction in virtual environments (i.e. providing adaptive personalized 3D
interaction techniques). Keywords: virtual environments; adaptation; 3D interaction techniques | |||