| Analyzing User Modeling on Twitter for Personalized News Recommendations | | BIBAK | Full-Text | 1-12 | |
| Fabian Abel; Qi Gao; Geert-Jan Houben; Ke Tao | |||
| How can micro-blogging activities on Twitter be leveraged for user modeling
and personalization? In this paper we investigate this question and introduce a
framework for user modeling on Twitter which enriches the semantics of Twitter
messages (tweets) and identifies topics and entities (e.g. persons, events,
products) mentioned in tweets. We analyze how strategies for constructing
hashtag-based, entity-based or topic-based user profiles benefit from semantic
enrichment and explore the temporal dynamics of those profiles. We further
measure and compare the performance of the user modeling strategies in context
of a personalized news recommendation system. Our results reveal how semantic
enrichment enhances the variety and quality of the generated user profiles.
Further, we see how the different user modeling strategies impact
personalization and discover that the consideration of temporal profile
patterns can improve recommendation quality. Keywords: user modeling; twitter; semantics; personalization | |||
| Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems | | BIBAK | Full-Text | 13-24 | |
| Ryan S. J. d. Baker; Zachary A. Pardos; Sujith M. Gowda; Bahador B. Nooraei; Neil T. Heffernan | |||
| Over the last decades, there have been a rich variety of approaches towards
modeling student knowledge and skill within interactive learning environments.
There have recently been several empirical comparisons as to which types of
student models are better at predicting future performance, both within and
outside of the interactive learning environment. However, these comparisons
have produced contradictory results. Within this paper, we examine whether
ensemble methods, which integrate multiple models, can produce prediction
results comparable to or better than the best of nine student modeling
frameworks, taken individually. We ensemble model predictions within a
Cognitive Tutor for Genetics, at the level of predicting knowledge
action-by-action within the tutor. We evaluate the predictions in terms of
future performance within the tutor and on a paper post-test. Within this data
set, we do not find evidence that ensembles of models are significantly better.
Ensembles of models perform comparably to or slightly better than the best
individual models, at predicting future performance within the tutor software.
However, the ensembles of models perform marginally significantly worse than
the best individual models, at predicting post-test performance. Keywords: student modeling; ensemble methods; Bayesian Knowledge-Tracing; Performance
Factors Analysis; Cognitive Tutor | |||
| Creating Personalized Digital Human Models of Perception for Visual Analytics | | BIBAK | Full-Text | 25-37 | |
| Mike Bennett; Aaron Quigley | |||
| Our bodies shape our experience of the world, and our bodies influence what
we design. How important are the physical differences between people? Can we
model the physiological differences and use the models to adapt and personalize
designs, user interfaces and artifacts? Within many disciplines Digital Human
Models and Standard Observer Models are widely used and have proven to be very
useful for modeling users and simulating humans. In this paper, we create
personalized digital human models of perception (Individual Observer Models),
particularly focused on how humans see. Individual Observer Models capture how
our bodies shape our perceptions. Individual Observer Models are useful for
adapting and personalizing user interfaces and artifacts to suit individual
users' bodies and perceptions. We introduce and demonstrate an Individual
Observer Model of human eyesight, which we use to simulate 3600 biologically
valid human eyes. An evaluation of the simulated eyes finds that they see eye
charts the same as humans. Also demonstrated is the Individual Observer Model
successfully making predictions about how easy or hard it is to see visual
information and visual designs. The ability to predict and adapt visual
information to maximize how effective it is is an important problem in visual
design and analytics. Keywords: virtual humans; physiology modeling; computational user model; individual
differences; human vision; digital human model | |||
| Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects | | BIBA | Full-Text | 38-49 | |
| John Champaign; Jie Zhang; Robin Cohen | |||
| In this paper, we examine a challenge that arises in the application of peer-based tutoring: coping with inappropriate advice from peers. We examine an environment where students are presented with those learning objects predicted to improve their learning (on the basis of the success of previous, like-minded students) but where peers can additionally inject annotations. To avoid presenting annotations that would detract from student learning (e.g. those found confusing by other students) we integrate trust modeling, to detect over time the reputation of the annotation (as voted by previous students) and the reputability of the annotator. We empirically demonstrate, through simulation, that even when the environment is populated with a large number of poor annotations, our algorithm for directing the learning of the students is effective, confirming the value of our proposed approach for student modeling. In addition, the research introduces a valuable integration of trust modeling into educational applications. | |||
| Modeling Mental Workload Using EEG Features for Intelligent Systems | | BIBAK | Full-Text | 50-61 | |
| Maher Chaouachi; Imène Jraidi; Claude Frasson | |||
| Endowing systems with abilities to assess a user's mental state in an
operational environment could be useful to improve communication and
interaction methods. In this work we seek to model user mental workload using
spectral features extracted from electroencephalography (EEG) data. In
particular, data were gathered from 17 participants who performed different
cognitive tasks. We also explore the application of our model in a non
laboratory context by analyzing the behavior of our model in an educational
context. Our findings have implications for intelligent tutoring systems
seeking to continuously assess and adapt to a learner's state. Keywords: cognitive workload; EEG; ITS | |||
| Context-Dependent Feedback Prioritisation in Exploratory Learning Revisited | | BIBAK | Full-Text | 62-74 | |
| Mihaela Cocea; George D. Magoulas | |||
| The open nature of exploratory learning leads to situations when feedback is
needed to address several conceptual difficulties. Not all, however, can be
addressed at the same time, as this would lead to cognitive overload and
confuse the learner rather than help him/her. To this end, we propose a
personalised context-dependent feedback prioritisation mechanism based on
Analytic Hierarchy Process (AHP) and Neural Networks (NN). AHP is used to
define feedback prioritisation as a multi-criteria decision-making problem,
while NN is used to model the relation between the criteria and the order in
which the conceptual difficulties should be addressed. When used alone, AHP
needs a large amount of data from experts to cover all possible combinations of
the criteria, while the AHP-NN synergy leads to a general model that outputs
results for any such combination. This work was developed and tested in an
exploratory learning environment for mathematical generalisation called
eXpresser. Keywords: context-dependent personalised feedback; feedback prioritisation;
exploratory learning; analytic hierarchy process; neural networks | |||
| Performance Comparison of Item-to-Item Skills Models with the IRT Single Latent Trait Model | | BIBAK | Full-Text | 75-86 | |
| Michel C. Desmarais | |||
| Assessing a learner's mastery of a set of skills is a fundamental issue in
intelligent learning environments. We compare the predictive performance of two
approaches for training a learner model with domain data. One is based on the
principle of building the model solely from observable data items, such as
exercises or test items. Skills modelling is not part of the training phase,
but instead dealt with at later stage. The other approach incorporates a single
latent skill in the model. We compare the capacity of both approaches to
accurately predict item outcome (binary success or failure) from a subset of
item outcomes. Three types of item-to-item models based on standard Bayesian
modeling algorithms are tested: (1) Naive Bayes, (2) Tree-Augmented Naive Bayes
(TAN), and (3) a K2 Bayesian Classifier. Their performance is compared to the
widely used IRT-2PL approach which incorporates a single latent skill. The
results show that the item-to-item approaches perform as well, or better than
the IRT-2PL approach over 4 widely different data sets, but the differences
vary considerably among the data sets. We discuss the implications of these
results and the issues relating to the practical use of item-to-item models. Keywords: IRT; Bayesian Models; TAN; Learner models | |||
| Hybrid User Preference Models for Second Life and OpenSimulator Virtual Worlds | | BIBAK | Full-Text | 87-98 | |
| Joshua Eno; Gregory Stafford; Susan Gauch; Craig W. Thompson | |||
| Virtual world user models have similarities with hypertext system user
models. User knowledge and preferences may be derived from the locations users
visit or recommend. The models can represent topics of interest for the user
based on the subject or content of visited locations, and corresponding
location models can enable matching between users and locations. However,
virtual worlds also present challenges and opportunities that differ from
hypertext worlds. Content collection for a cross-world search and
recommendation service may be more difficult in virtual worlds, and there is
less text available for analysis. In some cases, though, extra information is
available to add to user and content profiles enhance the matching ability of
the system. In this paper, we present a content collection system for Second
Life and OpenSimulator virtual worlds, as well as user and location models
derived from the collected content. The models incorporate text, social
proximity, and metadata attributes to create hybrid user models for
representing user interests and preferences. The models are evaluated based on
their ability to match content popularity and observed user behavior. Keywords: Content Models; Social Models; Virtual Worlds; Personalization;
Recommendations | |||
| Recipe Recommendation: Accuracy and Reasoning | | BIBAK | Full-Text | 99-110 | |
| Jill Freyne; Shlomo Berkovsky; Gregory Smith | |||
| Food and diet are complex domains for recommender technology, but the need
for systems that assist users in embarking on and engaging with healthy living
programs has never been more real. One key to sustaining long term engagement
with eHealth services is the provision of tools, which assist and train users
in planning correctly around the areas of diet and exercise. These tools
require an understanding of user reasoning as well as user needs and are ideal
application areas for recommender and personalization technologies. Here, we
report on a large scale analysis of real user ratings on a set of recipes in
order to judge the applicability and practicality of a number of
personalization algorithms. Further to this, we report on apparent user
reasoning patterns uncovered in rating data supplied for recipes and suggest
ways to exploit this reasoning understanding in the recommendation process. Keywords: Collaborative filtering; content-based; machine learning; recipes;
personalization | |||
| Tag-Based Resource Recommendation in Social Annotation Applications | | BIBA | Full-Text | 111-122 | |
| Jonathan Gemmell; Thomas Schimoler; Bamshad Mobasher; Robin Burke | |||
| Social annotation systems enable the organization of online resources with user-defined keywords. The size and complexity of these systems make them excellent platforms for the application of recommender systems, which can provide personalized views of complex information spaces. Many researchers have concentrated on the important problem of tag recommendation. Less attention has been paid to the recommendation of resources in the context of social annotation systems. In this paper, we examine the specific case of tag-based resource recommendation and propose a linear-weighted hybrid for the task. Using six real world datasets, we show that our algorithm is more effective than other more mathematically complex techniques. | |||
| The Impact of Rating Scales on User's Rating Behavior | | BIBA | Full-Text | 123-134 | |
| Cristina Gena; Roberto Brogi; Federica Cena; Fabiana Vernero | |||
| As showed in a previous work, different users show different preferences with respect to the rating scales to use for evaluating items in recommender systems. Thus in order to promote users' participation and satisfaction with recommender systems, we propose to allow users to choose the rating scales to use. Thus, recommender systems should be able to deal with ratings coming from heterogeneous scales in order to produce correct recommendations. In this paper we present two user studies that investigate the role of rating scales on user's rating behavior, showing that the rating scales have their own "personality" and mathematical normalization is not enough to cope with mapping among different rating scales. | |||
| Looking Beyond Transfer Models: Finding Other Sources of Power for Student Models | | BIBAK | Full-Text | 135-146 | |
| Yue Gong; Joseph E. Beck | |||
| Student modeling plays an important role in educational research. Many
techniques have been developed focusing on accurately estimating student
performances. In this paper, using Performance Factors Analysis as our
framework, we examine what components of the model enable us to better predict,
and consequently better understand, student performance. Using transfer models
to predict is very common across different student modeling techniques, as
student proficiencies on those required skills are believed, to a large degree,
to determine student performance. However, we found that problem difficulty is
an even more important predictor than student knowledge of the required skills.
In addition, we found that using student proficiencies across all skills works
better than just using those skills thought relevant by the transfer model. We
tested our proposed models with two transfer models of fine- and coarse-grain
sizes; the results suggest that the improvement is not simply an illusion due
to possible mistakes in associating skills with problems. Keywords: performance factors analysis; question difficulty; student overall
proficiencies; predicting student performance | |||
| Using Browser Interaction Data to Determine Page Reading Behavior | | BIBAK | Full-Text | 147-158 | |
| David Hauger; Alexandros Paramythis; Stephan Weibelzahl | |||
| The main source of information in most adaptive hypermedia systems are
server monitored events such as page visits and link selections. One drawback
of this approach is that pages are treated as "monolithic" entities, since the
system cannot determine what portions may have drawn the user's attention.
Departing from this model, the work described here demonstrates that
client-side monitoring and interpretation of users' interactive behavior (such
as mouse moves, clicks and scrolling) allows for detailed and significantly
accurate predictions on what sections of a page have been looked at. More
specifically, this paper provides a detailed description of an algorithm
developed to predict which paragraphs of text in a hypertext document have been
read, and to which extent. It also describes the user study, involving
eye-tracking for baseline comparison, that served as the basis for the
algorithm. Keywords: interaction monitoring; modeling algorithm; eye-tracking; empirical study | |||
| A User Interface for Semantic Competence Profiles | | BIBAK | Full-Text | 159-170 | |
| Martin Hochmeister; Johannes Daxböck | |||
| Competence management systems are increasingly based on ontologies
representing competences within a certain domain. Most of these systems
represent a user's competence profile by means of an ontological structure.
Such semantic competence profiles, often structured as a hierarchy of
competences, are difficult to navigate for self-assessment purposes. The more
competences a user profile holds, the more challenging the comprehensive
presentation of profile data is. In this paper, we present an integrated user
interface that supports users during competence self-assessment and facilitates
a clear presentation of their semantic competence profiles. For evaluation, we
conducted a usability study with 19 students at university. The results show
that users were mostly satisfied with the usability of the interface that also
represents a promising approach for efficient competence self-assessment. Keywords: User Interface; User Profile; Semantic Competence Profile; Profile Editing;
Ontology | |||
| Open Social Student Modeling: Visualizing Student Models with Parallel IntrospectiveViews | | BIBAK | Full-Text | 171-182 | |
| I-Han Hsiao; Fedor Bakalov; Peter Brusilovsky; Birgitta König-Ries | |||
| This paper explores a social extension of open student modeling that we call
open social student modeling. We present a specific implementation of this
approach that uses parallel IntrospectiveViews to visualize models representing
student progress with QuizJET parameterized self-assessment questions for Java
programming. The interface allows visualizing not only the student's own model,
but also displaying parallel views on the models of their peers and the
cumulative model of the entire class or group. The system was evaluated in a
semester-long classroom study. While the use of the system was non-mandatory,
the parallel IntrospectiveViews interface caused an increase in all of the
usage parameters in comparison to a regular portal-based access, which allowed
the student to achieve a higher success rate in answering the questions. The
collected data offer some evidence that a combination of traditional
personalized guidance with social guidance was more effective than personalized
guidance alone. Keywords: Open User Model; Visualization; Parameterized Self-Assessment; Open Student
Model | |||
| Location-Adapted Music Recommendation Using Tags | | BIBAK | Full-Text | 183-194 | |
| Marius Kaminskas; Francesco Ricci | |||
| Context-aware music recommender systems are capable to suggest music items
taking into consideration contextual conditions, such as the user mood or
location, that may influence the user preferences at a particular moment. In
this paper we consider a particular kind of context aware recommendation task
-- selecting music content that fits a place of interest (POI). To address this
problem we have used emotional tags attached by a users' population to both
music and POIs. Moreover, we have considered a set of similarity metrics for
tagged resources to establish a match between music tracks and POIs. In order
to test our hypothesis, i.e., that the users will reckon that a music track
suits a POI when this track is selected by our approach, we have designed a
live user experiment where subjects are repeatedly presented with POIs and a
selection of music tracks, some of them matching the presented POI and some
not. The results of the experiment show that there is a strong overlap between
the users' selections and the best matching music that is recommended by the
system for a POI. Keywords: recommender systems; location-aware; context; music; social tagging;
emotions | |||
| Leveraging Collaborative Filtering to Tag-Based Personalized Search | | BIBAK | Full-Text | 195-206 | |
| Heung-Nam Kim; Majdi Rawashdeh; Abdulmotaleb El Saddik | |||
| In recent years, social media services with social tagging have become
tremendously popular. Because users are no longer mere consumers of content,
social Web users have been overwhelmed by the huge numbers of social content
available. For tailoring search results, in this paper, we look into the
potential of social tagging in social media services. By leveraging
collaborative filtering, we propose a new search model to enhance not only
retrieval accuracy but also retrieval coverage. Our approach first computes
latent preferences of users on tags from other similar users, as well as latent
annotations of tags for items from other similar items. We then apply the
latency of tags to a tag-based personalized ranking depending on individual
users. Experimental results demonstrate the feasibility of our method for
personalized searches in social media services. Keywords: Personalized Search; Social Tagging; Collaborative Filtering | |||
| Modelling Symmetry of Activity as an Indicator of Collocated Group Collaboration | | BIBAK | Full-Text | 207-218 | |
| Roberto Martinez; Judy Kay; James R. Wallace; Kalina Yacef | |||
| There are many contexts where it would be helpful to model the collaboration
of a group. In learning settings, this is important for classroom teachers and
for students learning collaboration skills. Our approach exploits the digital
and audio footprints of the users' actions at collocated settings to
automatically build a model of symmetry of activity. This paper describes our
theoretical model of collaborative learning and how we implemented it. We use
the Gini coefficient as a statistical indicator of symmetry of activity, which
is itself an important indicator of collaboration. We built this model from a
small-scale qualitative study based on concept mapping at an interactive
tabletop. We then evaluated the model using a larger scale study based on a
corpus of coded data from a multi-display groupware collocated setting. Our key
contributions are the model of symmetry of activity as a foundation for
modelling collaboration within groups that should have egalitarian
participation, the operationalisation of the model and validation of the
approach on both a small-scale qualitative study and a larger scale
quantitative corpus of data. Keywords: tabletop; group modelling; groupware; collaborative learning; collocated
collaboration; clustering | |||
| A Dynamic Sliding Window Approach for Activity Recognition | | BIBAK | Full-Text | 219-230 | |
| Javier Ortiz Laguna; Angel García Olaya; Daniel Borrajo | |||
| Human activity recognition aims to infer the actions of one or more persons
from a set of observations captured by sensors. Usually, this is performed by
following a fixed length sliding window approach for the features extraction
where two parameters have to be fixed: the size of the window and the shift. In
this paper we propose a different approach using dynamic windows based on
events. Our approach adjusts dynamically the window size and the shift at every
step. Using our approach we have generated a model to compare both approaches.
Experiments with public datasets show that our method, employing simpler
models, is able to accurately recognize the activities, using fewer instances,
and obtains better results than the approaches used by the datasets authors. Keywords: Human Activity Recognition; Sliding Window; Sensor Networks; Wearable
Systems; Ubiquitous Computing | |||
| Early Detection of Potential Experts in Question Answering Communities | | BIBAK | Full-Text | 231-242 | |
| Aditya Pal; Rosta Farzan; Joseph A. Konstan; Robert E. Kraut | |||
| Question answering communities (QA) are sustained by a handful of experts
who provide a large number of high quality answers. Identifying these experts
during the first few weeks of their joining the community can be beneficial as
it would allow community managers to take steps to develop and retain these
potential experts. In this paper, we explore approaches to identify potential
experts as early as within the first two weeks of their association with the
QA. We look at users' behavior and estimate their motivation and ability to
help others. These qualities enable us to build classification and ranking
models to identify users who are likely to become experts in the future. Our
results indicate that the current experts can be effectively identified from
their early behavior. We asked community managers to evaluate the potential
experts identified by our algorithm and their analysis revealed that quite a
few of these users were already experts or on the path of becoming experts. Our
retrospective analysis shows that some of these potential experts had already
left the community, highlighting the value of early identification and
engagement. Keywords: Question Answering; Potential Experts; Expert Identification | |||
| KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model | | BIBAK | Full-Text | 243-254 | |
| Zachary A. Pardos; Neil T. Heffernan | |||
| Many models in computer education and assessment take into account
difficulty. However, despite the positive results of models that take
difficulty in to account, knowledge tracing is still used in its basic form due
to its skill level diagnostic abilities that are very useful to teachers. This
leads to the research question we address in this work: Can KT be effectively
extended to capture item difficulty and improve prediction accuracy? There have
been a variety of extensions to KT in recent years. One such extension was
Baker's contextual guess and slip model. While this model has shown positive
gains over KT in internal validation testing, it has not performed well
relative to KT on unseen in-tutor data or post-test data, however, it has
proven a valuable model to use alongside other models. The contextual guess and
slip model increases the complexity of KT by adding regression steps and
feature generation. The added complexity of feature generation across datasets
may have hindered the performance of this model. Therefore, one of the aims of
our work here is to make the most minimal of modifications to the KT model in
order to add item difficulty and keep the modification limited to changing the
topology of the model. We analyze datasets from two intelligent tutoring
systems with KT and a model we have called KT-IDEM (Item Difficulty Effect
Model) and show that substantial performance gains can be achieved with this
minor modification that incorporates item difficulty. Keywords: Knowledge Tracing; Bayesian Networks; Item Difficulty; User Modeling; Data
Mining | |||
| Walk the Talk: Analyzing the Relation between Implicit and Explicit Feedback for Preference Elicitation | | BIBA | Full-Text | 255-268 | |
| Denis Parra; Xavier Amatriain | |||
| Most of the approaches for understanding user preferences or taste are based on having explicit feedback from users. However, in many real-life situations we need to rely on implicit feedback. To analyze the relation between implicit and explicit feedback, we conduct a user experiment in the music domain. We find that there is a strong relation between implicit feedback and ratings. We analyze the effect of context variables on the ratings and find that recentness of interaction has a significant effect. We also analyze several user variables. Finally, we propose a simple linear model that relates these variables to the rating we can expect to an item. Such mapping would allow to easily adapt any existing approach that uses explicit feedback to the implicit case and combine both kinds of feedback. | |||
| Finding Someone You Will Like and Who Won't Reject You | | BIBA | Full-Text | 269-280 | |
| Luiz Augusto Pizzato; Tomek Rej; Kalina Yacef; Irena Koprinska; Judy Kay | |||
| This paper explores ways to address the problem of the high cost problem of poor recommendations in reciprocal recommender systems. These systems recommend one person to another and require that both people like each other for the recommendation to be successful. A notable example, and the focus of our experiments is online dating. In such domains, poor recommendations should be avoided as they cause users to suffer repeated rejection and abandon the site. This paper describes our experiments to create a recommender based on two classes of models: one to predict who each user will like; the other to predict who each user will dislike. We then combine these models to generate recommendations for the user. This work is novel in exploring modelling both people's likes and dislikes and how to combine these to support a reciprocal recommendation, which is important for many domains, including online dating, employment, mentor-mentee matching and help-helper matching. Using a negative and a positive preference model in a combined manner, we improved the success rate of reciprocal recommendations by 18% while, at the same time, reducing the failure rate by 36% for the top-1 recommendations in comparison to using the positive model of preference alone. | |||
| Personalizing the Theme Park: Psychometric Profiling and Physiological Monitoring | | BIBAK | Full-Text | 281-292 | |
| Stefan Rennick-Egglestone; Amanda Whitbrook; Caroline Leygue; Julie Greensmith; Brendan Walker; Steve Benford; Holger Schnädelbach; Stuart Reeves; Joe Marshall; David Kirk; Paul Tennent; Ainoje Irune; Duncan Rowland | |||
| Theme parks are important and complex forms of entertainment, with a broad
user-base, and with a substantial economic impact. In this paper, we present a
case study of an existing theme park, and use this to motivate two research
challenges in relation to user-modeling and personalization in this
environment: developing recommender systems to support theme park visits, and
developing rides that are personalized to the users who take part in them. We
then provide an analysis, drawn from a real-world study on an existing ride,
which illustrates the efficacy of psychometric profiling and physiological
monitoring in relation to these challenges. We conclude by discussing further
research work that could be carried out within the theme park, but motivate
this research by considering the broader contribution to user-modeling issues
that it could make. As such, we present the theme park as a microcosm which is
amenable to research, but which is relevant in a much broader setting. Keywords: Psychometrics; physiological monitoring; theme park | |||
| Recognising and Recommending Context in Social Web Search | | BIBAK | Full-Text | 293-304 | |
| Zurina Saaya; Barry Smyth; Maurice Coyle; Peter Briggs | |||
| In this paper we focus on an approach to social search, HeyStaks that is
designed to integrate with mainstream search engines such as Google, Yahoo and
Bing. HeyStaks is motivated by the idea that Web search is an inherently social
or collaborative activity. Heystaks users search as normal but benefit from
collaboration features, allowing searchers to better organise and share their
search experiences. Users can create and share repositories of search knowledge
(so-called search staks) in order to benefit from the searches of friends and
colleagues. As such search staks are community-based information resources. A
key challenge for HeyStaks is predicting which search stak is most relevant to
the users current search context and in this paper we focus on this so-called
stak recommendation issue by looking at a number of different approaches to
profiling and recommending community-search knowledge. Keywords: social search; context recommendation | |||
| Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering | | BIBAK | Full-Text | 305-316 | |
| Yue Shi; Martha Larson; Alan Hanjalic | |||
| Recommender systems generally face the challenge of making predictions using
only the relatively few user ratings available for a given domain. Cross-domain
collaborative filtering (CF) aims to alleviate the effects of this data
sparseness by transferring knowledge from other domains. We propose a novel
algorithm, Tag-induced Cross-Domain Collaborative Filtering (TagCDCF), which
exploits user-contributed tags that are common to multiple domains in order to
establish the cross-domain links necessary for successful cross-domain CF.
TagCDCF extends the state-of-the-art matrix factorization by introducing a
constraint involving tag-based similarities between pairs of users and pairs of
items across domains. The method requires no common users or items across
domains. Using two publicly available CF data sets as different domains, we
experimentally demonstrate that TagCDCF substantially outperforms other
state-of-the-art single domain CF and cross-domain CF approaches. Additional
experiments show that TagCDCF addresses data sparseness and illustrate the
influence of the number of tags used by users in both domains. Keywords: Collaborative filtering; cross domain collaborative filtering; matrix
factorization; tag; recommender systems | |||
| User Modeling -- A Notoriously Black Art | | BIBAK | Full-Text | 317-328 | |
| Michael Yudelson; Philip I. Pavlik; Kenneth R. Koedinger | |||
| This paper is intended as guidance for those who are familiar with user
modeling field but are less fluent in statistical methods. It addresses
potential problems with user model selection and evaluation, that are often
clear to expert modelers, but are not obvious for others. These problems are
frequently a result of a falsely straightforward application of statistics to
user modeling (e.g. over-reliance on model fit metrics). In such cases,
absolute trust in arguably shallow model accuracy measures could lead to
selecting models that are hard-to-interpret, less meaningful, over-fit, and
less generalizable. We offer a list of questions to consider in order to avoid
these modeling pitfalls. Each of the listed questions is backed by an
illustrative example based on the user modeling approach called Performance
Factors Analysis (PFA) [9]. Keywords: User modeling; educational data mining; model selection; model complexity;
model parsimony | |||
| Selecting Items of Relevance in Social Network Feeds | | BIBA | Full-Text | 329-334 | |
| Shlomo Berkovsky; Jill Freyne; Stephen Kimani; Gregory Smith | |||
| The success of online social networking systems has revolutionised online sharing and communication, however it has also contributed significantly to the infamous information overload problem. Social Networking systems aggregate network activities into chronologically ordered lists, Network Feeds, as a way of summarising network activity for its users. Unfortunately, these feeds do not take into account the interests of the user viewing them or the relevance of each feed item to the viewer. Consequently individuals often miss out on important updates. This work aims to reduce the burden on users of identifying relevant feed items by exploiting observed user interactions with content and people on the network and facilitates the personalization of network feeds in a manner which promotes relevant activities. We present the results of a large scale live evaluation which shows that personalized feeds are more successful at attracting user attention than non-personalized feeds. | |||
| Enhancing Traditional Local Search Recommendations with Context-Awareness | | BIBAK | Full-Text | 335-340 | |
| Claudio Biancalana; Andrea Flamini; Fabio Gasparetti; Alessandro Micarelli; Samuele Millevolte; Giuseppe Sansonetti | |||
| Traditional desktop search paradigm often does not fit mobile contexts.
Common mobile devices provide impoverished mechanisms for text entry and small
screens are able to offer only a limited set of options, therefore the users
are not usually able to specify their needs. On a different note, mobile
technologies have become part of the everyday life as shown by the estimate of
one billion of mobile broadband subscriptions in 2011.
This paper describes an approach to make context-aware mobile interaction available in scenarios where users might be looking for categories of points of interest (POIs), such as cultural events and restaurants, through remote location-based services. Empirical evaluations shows how rich representations of user contexts has the chance to increase the relevance of the retrieved POIs. Keywords: context-awareness; local search; location-based services; mobile devices | |||
| Gender Differences and the Value of Choice in Intelligent Tutoring Systems | | BIBA | Full-Text | 341-346 | |
| Derek T. Green; Thomas J. Walsh; Paul R. Cohen; Carole R. Beal; Yu-Han Chang | |||
| Students interacted with an intelligent tutoring system to learn grammatical rules for an artificial language. Six tutoring policies were explored. One, based on a Dynamic Bayes' Network model of skills, was learned from the performance of previous students. Overall, this policy and other intelligent policies outperformed random policies. Some policies allowed students to choose one of three problems to work on, while others presented a single problem at each iteration. The benefit of choice was not apparent in group statistics; however, there was a strong interaction with gender. Overall, women learned less than men, but they learned different amounts in the choice and no choice conditions, whereas men seemed unaffected by choice. We explore reasons for these interactions between gender, choice and learning. | |||
| Towards Understanding How Humans Teach Robots | | BIBA | Web Page | 347-352 | |
| Tasneem Kaochar; Raquel Torres Peralta; Clayton T. Morrison; Ian R. Fasel; Thomas J. Walsh; Paul R. Cohen | |||
| Our goal is to develop methods for non-experts to teach complex behaviors to autonomous agents (such as robots) by accommodating "natural" forms of human teaching. We built a prototype interface allowing humans to teach a simulated robot a complex task using several techniques and report the results of 44 human participants using this interface. We found that teaching styles varied considerably but can be roughly categorized based on the types of interaction, patterns of testing, and general organization of the lessons given by the teacher. Our study contributes to a better understanding of human teaching patterns and makes specific recommendations for future human-robot interaction systems. | |||
| Towards Open Corpus Adaptive Hypermedia: A Study of Novelty Detection Approaches | | BIBAK | Full-Text | 353-358 | |
| Yi-ling Lin; Peter Brusilovsky | |||
| Classic adaptive hypermedia systems are able to track a user's knowledge of
the subject and use it to evaluate the novelty and difficulty of content
encountered by the user. Our goal is to implement this functionality in an open
corpus context where a domain model is not available nor is the content indexed
with domain concepts. We examine methods for novelty measurement based on
automatic text analysis. To compare these methods, we use an evaluation
approach based on knowledge encapsulated in the structure of a textbook. Our
study shows that a knowledge accumulation method adopted from the domain of
intelligent tutoring systems offers a more meaningful novelty measurement than
methods adapted from the area of personalized information retrieval. Keywords: Novelty detection; knowledge modeling; personalization | |||
| 4MALITY: Coaching Students with Different Problem-Solving Strategies Using an Online Tutoring System | | BIBAK | Full-Text | 359-364 | |
| Leena Razzaq; Robert W. Maloy; Sharon Edwards; David Marshall; Ivon Arroyo; Beverley P. Woolf | |||
| 4-coach Mathematics Active Learning Intelligent Tutoring sYstem (4MALITY) is
a web-based intelligent tutoring system for 3rd, 4th, and 5th grade students
who are learning math content from the state of Massachusetts (USA) required
curriculum framework. The goal of 4MALITY is to personalize help for students
by offering them problem-solving strategies authored from multiple points of
view. Four virtual coaches (Estella Explainer, Chef Math Bear, How-to Hound,
and Visual Vicuna) are designed to capture the character and content of these
different problem-solving approaches with language, computation, strategy, and
visual hints. A preliminary study was run with 102 students in fourth and fifth
grade math classrooms over a period of two months. The results showed that the
effect of using 4MALITY produced a statistically significant increase in
post-test scores. We explored student performance, help-seeking behavior and
meta-cognitive strategies by gender and math ability and report these results. Keywords: personalizing help; intelligent tutoring systems; problem-solving strategies | |||
| Capitalizing on Uncertainty, Diversity and Change by Online Individualization of Functionality | | BIBAK | Full-Text | 365-376 | |
| Reza Razavi | |||
| Uncertainty, diversity and change create endless streams of unexpected new
opportunities. To seize those opportunities, new web-based systems are emerging
that enforce participative design and empower end-users to take actively part
in the creation and maintenance of functionality that fits specific needs and
conditions. For example, Yahoo! Pipes is a "participative site" with visual
online programming means for defining and readily deploying web-based services
that fetch, aggregate and process web feeds. Standard and dedicated engineering
tools for developing such web sites are however yet to be invented. This paper
describes our software platform for their development by reuse and extension,
while meeting the requirements of end-user accessibility, expressivity,
interpretability, web compatibility, shareability and traceability as they
appear in person-centric areas like Ambient Assisted Living. We allow dynamic
and user-driven individualization of functionality by capturing at runtime, and
processing complex interaction patterns that involve end-users, their physical
environment and software components. Keywords: Model-based Personalization; Web Technology; User-Generated Services;
Adaptive Object-Model; Design Pattern; Framework; Pull Model | |||
| Prediction of Socioeconomic Levels Using Cell Phone Records | | BIBA | Full-Text | 377-388 | |
| Victor Soto; Vanessa Frias-Martinez; Jesus Virseda; Enrique Frias-Martinez | |||
| The socioeconomic status of a population or an individual provides an understanding of its access to housing, education, health or basic services like water and electricity. In itself, it is also an indirect indicator of the purchasing power and as such a key element when personalizing the interaction with a customer, especially for marketing campaigns or offers of new products. In this paper we study if the information derived from the aggregated use of cell phone records can be used to identify the socioeconomic levels of a population. We present predictive models constructed with SVMs and Random Forests that use the aggregated behavioral variables of the communication antennas to predict socioeconomic levels. Our results show correct prediction rates of over 80% for an urban population of around 500,000 citizens. | |||
| User Perceptions of Adaptivity in an Interactive Narrative | | BIBAK | Full-Text | 389-400 | |
| Karen Tanenbaum; Marek Hatala; Joshua Tanenbaum | |||
| We present results from a user study of the Reading Glove version 2.0, a
combination wearable and tabletop interactive narrative system. The system was
designed to study user perceptions of adaptivity. The system's reasoning engine
guides users through the story using three different recommendation modes:
random recommendations, story content-based recommendations, and user model
based recommendations. We look at the differences in user behaviour and
experience across the three recommendation systems, using information from
system logs and user surveys and interviews. Keywords: Adaptivity; User Modeling; Expert Systems; Interactive Narrative | |||
| Performance Prediction in Recommender Systems | | BIBAK | Full-Text | 401-404 | |
| Alejandro Bellogín | |||
| Research on Recommender Systems has barely explored the issue of adapting a
recommendation strategy to the user's information available at a certain time.
In this thesis, we introduce a component that allows building dynamic
recommendation strategies, by reformulating the performance prediction problem
in the area of Information Retrieval to that of recommender systems. More
specifically, we investigate a number of adaptations of the query clarity
predictor in order to infer the ambiguity in user and item profiles. The
properties of each predictor are empirically studied by, first, checking the
correlation of the predictor output with a performance measure, and second, by
incorporating a performance predictor into a recommender system to produce a
dynamic strategy. Depending on how the predictor is integrated with the system,
we explore two different applications: dynamic user neighbour weighting and
hybrid recommendation. The performance of such dynamic strategies is examined
and compared with that of static ones. Keywords: recommender systems; performance prediction; query clarity; personalisation;
user modelling | |||
| Multi-perspective Context Modelling to Augment Adaptation in Simulated Learning Environments | | BIBA | Full-Text | 405-408 | |
| Dimoklis Despotakis | |||
| Simulated environments, where learners are involved in simulated situations that resemble actual activities, gain a growing popularity in professional training, and provide powerful experiential learning tools for developing soft skills in ill-defined domains[1]. Adaptation and personalization will play a key role in these environments[2]. | |||
| Situation Awareness in Neurosurgery: A User Modeling Approach | | BIBAK | Full-Text | 409-413 | |
| Shahram Eivazi | |||
| Situation awareness is a perception of the available information, events,
resources, and environment within a given time and space. Humans have limited
abilities to obtain and maintain situation awareness, as they need to carefully
orchestrate the available resources. A failure to maintain situation awareness
may lead to serious errors in human behavior. Investigation of the situation
awareness of neurosurgeons using cognitive architectures is a new and exciting
application of computational user modeling. Accurately modeling of the
surgeons' behavior and their mental states while they perform operations using
miniature instruments and movements require various implicit measures of the
surgeons' behavior. The user modeling community has been searching for such
data sources in other domains and have indicated that eye-tracking, as a
noninvasive methodology, can be used to enrich the user models and increase
their quality. In this research I will 1) investigate what are the constituents
of situation awareness during neurosurgery, 2) how eye-tracking methodologies
fit to created suitable user models of situation awareness, and 3) how data
should be processed, and what features of eye-tracking data work best. We
propose to use eye tracking techniques to develop a comprehensive computational
model of the surgeons' behavior. The model will be further interpreted, to
understand how information, events, and surgeons' actions will impact
neurosurgery operations. Keywords: User modeling; Eye-tracking; Machine learning; neurosurgery | |||
| Adaptive Active Learning in Recommender Systems | | BIBA | Full-Text | 414-417 | |
| Mehdi Elahi | |||
| Recommender Systems (RSs) generate personalized suggestions to users for items that may be interesting for them. Many RSs use the Collaborative Filtering (CF) technique, where the system gathers some information about the users by eliciting their ratings for items. To do so, the system may actively choose the items to present to the users to rate. This proactive approach is called Active Learning (AL), since the system actively search for relevant data before building any predictive model of the user interests. But, since not all the ratings will improve the accuracy in the same way, finding the best items to query the users for their ratings is challenging. In this work, we address this problem by reviewing some AL techniques and discussing their performance on the base of the experiments we made. | |||
| Extending Sound Sample Descriptions through the Extraction of Community Knowledge | | BIBAK | Full-Text | 418-421 | |
| Frederic Font; Xavier Serra | |||
| Sound and music online services driven by communities of users are filled
with large amounts of user-created content that has to be properly described.
In these services, typical sound and music modeling is performed using either
content-based or context-based strategies, but no special emphasis is given to
the extraction of knowledge from the community. We outline a research plan in
the context of Freesound.org and propose ideas about how audio clip sharing
sites could adapt and take advantage of particular user communities to improve
the descriptions of their content. Keywords: sound and music computing; online communities; folksonomies; emergent
semantics; freesound | |||
| Monitoring Contributions Online: A Reputation System to Model Expertise in Online Communities | | BIBAK | Full-Text | 422-425 | |
| Thieme Hennis | |||
| This document contains a brief description of my PhD research, with problem
definition, contribution to the field of reputation systems and user modeling,
and proposed solution. The proposed method and algorithm enable evaluation of
contributions in online knowledge-based communities. The innovation in the
approach is the use of authority and specifying reputation on the
keyword-level. Keywords: reputation; user-modeling; motivation; transitivity; context | |||
| Student Procedural Knowledge Inference through Item Response Theory | | BIBAK | Full-Text | 426-429 | |
| Manuel Hernando | |||
| This paper describes our research lines that focus on modeling and inferring
student procedural knowledge in Intelligent Tutoring Systems. Our proposal is
to apply Item Response Theory, a well-founded theory for declarative knowledge
assessment, to infer procedural knowledge in problem solving environments.
Therefore, we treat the problems as tests and the steps of problem solving as
options (or choices) in a question. An important feature of our system is that
it is not only based on an expert analysis, but also on data-driven techniques
so that it can collect the largest amount of students' problem solving
strategies as possible. Keywords: Student modeling; procedural knowledge; Item Response Theory | |||
| Differences on How People Organize and Think about Personal Information | | BIBAK | Full-Text | 430-433 | |
| Matjaz Kljun | |||
| Personal information management (PIM) is a study on how people handle
personal information to support their needs and tasks. In the last decade a lot
of studies focused on how people acquire, organize, maintain and retrieve
information from their information spaces. Results have led to many research
prototypes that tried to either augment present tools or integrate these
collections within entirely new designs. However, not much has changed in the
present tools, and hierarchies still prevail as the storage foundation. Our
research aims at understanding the difference between how people organize their
information in various applications and physical space and how they actually
think of this information in relation to tasks they have to accomplish. We
carried out a preliminary study and are currently finishing another study which
both show that there is a difference on how information is organized in formal
structures on computers and physical spaces and how it is thought of in users'
heads. These findings have motivated the design of an application that tries to
mimic the latter and adapts to current computer activities. Keywords: personal information management; task; information collection; mental links | |||
| Towards Contextual Search: Social Networks, Short Contexts and Multiple Personas | | BIBAK | Full-Text | 434-437 | |
| Tomáš Kramár | |||
| In this paper we present an approach to contextual search, based on the
automatically extracted metadata from visited documents. User model represents
user's interests as a combination of tags, keywords and named entities. Such
user model is further enhanced by automatically detected communities of similar
users, based on the similarities of their models. The user may belong to
multiple communities, each representing one of her possibly many personas --
roles or stereotypes, facets of her interests. We discuss further possibilities
of using this model to bring more fine-grained contextualization and search
improvement by using short contexts. Keywords: personalized search; search context; personas; social networks | |||
| Using Folksonomies for Building User Interest Profile | | BIBAK | Full-Text | 438-441 | |
| Harshit Kumar; Hong-Gee Kim | |||
| This work exploits folksonomy for building User Interest Profile (UIP) based
on user's search history. UIP is an indispensable source of knowledge which can
be exploited by intelligent systems for query recommendation, personalized
search, and web search result ranking etc. A UIP consist of a clustered list of
concepts and their weights. We show how to design, implement, and visualize
such a system, in practice, which aids in finding interesting relationships
between concepts and detect outliers, if any. The experiment reveals that UIP
not only captures user interests but also its context and results are very
promising. Keywords: User Profiling; Folksonomy; Clustering | |||
| Designing Trustworthy Adaptation on Public Displays | | BIBAK | Full-Text | 442-445 | |
| Ekaterina Kurdyukova | |||
| Adaptation on public displays brings certain advantages and risks. Due to
the implicit nature of adaptation, the users often miss the causality behind
the adaptive behavior. Moreover, a high degree of autonomy in adaptive displays
may leave the users with the feeling of control loss. Limited amount of
transparency and controllability leads to the loss of user trust. As a result,
the users feel insecure, frustrated, and are likely to abandon the system. The
research goal of this work is to optimize the system actions in a ubiquitous
display environment, in order make adaptation design transparent, controllable,
and thus trustworthy. By means of a decision-theoretic approach the user trust
can be assessed in different trust-critical contexts. The contexts describe the
changes in the environment that call for adaptation: privacy of content, social
setting, and accuracy of knowledge. The generated decisions enable the system
to maintain trust and keep interaction comfortable. Keywords: Adaptation; public displays; trust | |||
| Using Adaptive Empathic Responses to Improve Long-Term Interaction with Social Robots | | BIBAK | Full-Text | 446-449 | |
| Iolanda Leite | |||
| The goal of this research is to investigate the effects of empathy and
adaptive behaviour in long-term interaction between social robots and users. To
address this issue, we propose an action selection mechanism that will allow a
social robot to chose adaptive empathic responses, in the attempt to keep users
engaged over several interactions. Keywords: social robots; affective user modeling; empathy; adaptive feedback | |||
| A User Modeling Approach to Support Knowledge Work in Socio-computational Systems | | BIBAK | Full-Text | 450-453 | |
| Karin Schoefegger | |||
| The rise of socio-computational systems such as collaborative tagging
systems, which rely heavily on user-generated content and social interactions,
changed our way to learn and work. This work aims to explore the potentials of
those systems for supporting knowledge work in organizational and scientific
domains. Therefore, a user modeling approach will be developed which enables
personalized services to shape the content towards individual information needs
of novice, advanced and experienced knowledge workers. The novelty of this
approach is a modeling strategy which combines user modeling characteristics
from distinct research areas, the emergent properties of the
socio-computational environment as well as non-invasive knowledge diagnosis
methods based on the user's past interaction with the system. Keywords: User modeling; emergent semantics; work-integrated learning; personalized
services; collaborative tagging systems; knowledge work | |||
| Modeling Individuals with Learning Disabilities to Personalize a Pictogram-Based Instant Messaging Service | | BIBAK | Full-Text | 454-457 | |
| Pere Tuset-Peiró | |||
| Individuals with learning disabilities are excluded from the information and
knowledge society because information present in such media, as well as
software that enables access to it, does not meet their communication and
accessibility requirements. To improve this situation, we have developed and
evaluated an Instant Messaging (IM) service and client based on a pictographic
communication system, and with a user interface designed taking into account
their accessibility requirements. But the evaluation with individuals with
learning disabilities has pointed out the need to take into account the great
communications and computer skills diversity, even in groups with similar
disability levels. Therefore, in this paper we present our plans to model the
communication and accessibility requirements of individuals with learning
disabilities in order to develop a mechanism to automatically personalize the
IM client user interface and adapt it to their needs. Keywords: Learning disabilities; Pictographic Communication System; Instant Messaging
services; User modeling; Interface personalization | |||
| FamCHAI: An Adaptive Calendar Dialogue System | | BIBAK | Full-Text | 458-461 | |
| Ramin Yaghoubzadeh | |||
| The dissertation project FamCHAI aims at creating a 'calendar companion'
system in the form of a bidirectionally natural-language interactive scene with
a virtual agent, and exploring the effects of adaptation of the agent to
specific users both in terms of the support given (i.e. giving options the user
likes) and in communication (i.e. presentation in a form the user prefers, and
learning their idiosyncrasies for better understanding). Harnessing these
models, interactions will grow steadily more effective, comfortable and natural
for users. Keywords: Scheduling; Dialogue Models; Adaptivity in Conversation | |||