| Exploring the Potential of User Modeling Based on Mind Maps | | BIBAK | Full-Text | 3-17 | |
| Joeran Beel; Stefan Langer; Georgia Kapitsaki; Corinna Breitinger; Bela Gipp | |||
| Mind maps have not received much attention in the user modeling and
recommender system community, although mind maps contain rich information that
could be valuable for user-modeling and recommender systems. In this paper, we
explored the effectiveness of standard user-modeling approaches applied to mind
maps. Additionally, we develop novel user modeling approaches that consider the
unique characteristics of mind maps. The approaches are applied and evaluated
using our mind mapping and reference-management software Docear. Docear
displayed 430,893 research paper recommendations, based on 4,700 user mind
maps, from March 2013 to August 2014. The evaluation shows that standard user
modeling approaches are reasonably effective when applied to mind maps, with
click-through rates (CTR) between 1.16% and 3.92%. However, when adjusting user
modeling to the unique characteristics of mind maps, a higher CTR of 7.20%
could be achieved. A user study confirmed the high effectiveness of the mind
map specific approach with an average rating of 3.23 (out of 5), compared to a
rating of 2.53 for the best baseline. Our research shows that mind map-specific
user modeling has a high potential, and we hope that our results initiate a
discussion that encourages researchers to pursue research in this field and
developers to integrate recommender systems into their mind mapping tools. Keywords: Mind map; User modeling; Recommender systems | |||
| Modeling Motivation in a Social Network Game Using Player-Centric Traits and Personality Traits | | BIBAK | Full-Text | 18-30 | |
| Max V. Birk; Dereck Toker; Regan L. Mandryk; Cristina Conati | |||
| People are drawn to play different types of videogames and find enjoyment in
a range of gameplay experiences. Envisaging a representative game player or
persona allows game designers to personalize game content; however, there are
many ways to characterize players and little guidance on which approaches best
model player behavior and preference. To provide knowledge about how player
characteristics contribute to game experience, we investigate how personality
traits as well as player styles from the BrianHex model moderate the prediction
of player motivation with a social network game. Our results show that several
player characteristics impact motivation, expressed in terms of enjoyment and
effort. We also show that player enjoyment and effort, as predicted by our
models, impact players' in-game behaviors, illustrating both the predictive
power and practical utility of our models for guiding user adaptation. Keywords: User modeling; Personality; Player experience; Social network game; Linear
regression; Moderation; Motivation | |||
| Automatic Gaze-Based Detection of Mind Wandering with Metacognitive Awareness | | BIBAK | Full-Text | 31-43 | |
| Robert Bixler; Sidney D'Mello | |||
| Mind wandering (MW) is a ubiquitous phenomenon where attention involuntarily
shifts from task-related processing to task-unrelated thoughts. There is a need
for adaptive systems that can reorient attention when MW is detected due to its
detrimental effects on performance and productivity. This paper proposes an
automated gaze-based detector of self-caught MW (i.e., when users become
consciously aware that they are MW). Eye gaze data and self-reports of MW were
collected as 178 users read four instructional texts from a computer interface.
Supervised machine learning models trained on features extracted from users'
gaze fixations were used to detect pages where users caught themselves MW. The
best performing model achieved a user-independent kappa of .45 (accuracy of 74%
compared to a chance accuracy of 52%); the first ever demonstration of a
self-caught MW detector. An analysis of the features revealed that during MW,
users made more regression fixations, had longer saccades that crossed lines
more often, and had more uniform fixation durations, indicating a violation
from normal reading patterns. Applications of the MW detector are discussed. Keywords: Gaze tracking; Mind wandering; Affect detection; User modeling | |||
| The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling | | BIBAK | Full-Text | 44-55 | |
| Peter Brusilovsky; Sibel Somyürek; Julio Guerra; Roya Hosseini; Vladimir Zadorozhny | |||
| Open Student Modeling (OSM) is a popular technology that makes traditionally
hidden student models available to the learners for exploration. OSM is known
for its ability to increase student engagement, motivation, and knowledge
reflection. A recent extension of OSM known as Open Social Student Modeling
(OSSM) attempts to enhance cognitive aspects of OSM with social aspects by
allowing students to explore models of peer students or the whole class. In
this paper, we introduce MasteryGrids, a scalable OSSM interface and report the
results of a large-scale classroom study that explored the value of adding
social dimension to OSM. The results of the study reveal a remarkable engaging
potential of OSSM as well as its impact on learning effectiveness and user
attitude. Keywords: Open student modeling; Open social student modeling; Social visualization | |||
| MENTOR: A Physiologically Controlled Tutoring System | | BIBAK | Full-Text | 56-67 | |
| Maher Chaouachi; Imène Jraidi; Claude Frasson | |||
| In this paper we present a tutoring system that automatically sequences the
learning content according to the learners' mental states. The system draws on
techniques from Brain Computer Interface and educational psychology to
automatically adapt to changes in the learners' mental states such as attention
and workload using electroencephalogram (EEG) signals. The objective of this
system is to maintain the learner in a positive mental state throughout the
tutoring session by selecting the next pedagogical activity that fits the best
to his current state. An experimental evaluation of our approach involving two
groups of learners showed that the group who interacted with the mental
state-based adaptive version of the system obtained higher learning outcomes
and had a better learning experience than the group who interacted with a
non-adaptive version. Keywords: Intelligent tutoring system; Engagement; Workload; Real-time adaptive
system; EEG; Machine learning; Experience and affect | |||
| Context-Aware User Modeling Strategies for Journey Plan Recommendation | | BIBAK | Full-Text | 68-79 | |
| Victor Codina; Jose Mena; Luis Oliva | |||
| Popular journey planning systems, like Google Maps or Yahoo! Maps, usually
ignore user's preferences and context. This paper shows how we applied
context-aware recommendation technologies in an existing journey planning
mobile application to provide personalized and context-dependent
recommendations to users. We describe two different strategies for
context-aware user modeling in the journey planning domain. We present an
extensive performance comparison of the proposed strategies by conducting a
user-centric study in addition to a traditional offline evaluation method. Keywords: Recommender systems; Context-awareness; Personalized journey planning;
User-centric evaluation | |||
| MOOClm: User Modelling for MOOCs | | BIBAK | Full-Text | 80-91 | |
| Ronny Cook; Judy Kay; Bob Kummerfeld | |||
| Emerging MOOC platforms capture huge amounts of learner data. This paper
presents our MOOClm platform, for transforming data from MOOCs into independent
learner models that can drive personalisation and support reuse of the learner
model, for example in an Open Learner Model (OLM). We describe the MOOClm
architecture and demonstrate how we have used it to build OLMs. Keywords: MOOCs; Learner modelling; Open Learner Modelling (OLM); Learner model server | |||
| Dynamic Approaches to Modeling Student Affect and its Changing Role in Learning and Performance | | BIBAK | Full-Text | 92-103 | |
| Seth Corrigan; Tiffany Barkley; Zachary Pardos | |||
| We investigate the relation between students' affect, learning and
performance in the context of the ASSISTments online math tutoring system.
Moment-by-moment estimates of students' affective states derived from a series
of affect detectors accompany each student response within the tutoring system.
By applying a series modified factorial hidden Markov models that account for
students' affective state at the time of the given response and comparing the
models' performance to the standard Bayesian Knowledge Tracing (BKT) approach,
we evaluate the impact of affect on estimates of students' guess and slip
behavior. The investigation suggests a model based approach to improving
student models in the context of online tutoring systems. Keywords: Knowledge tracing; Emotion; Affect; Learning; Performance; Hidden Markov
models; Factorial hidden Markov models; Automated tutoring systems | |||
| Analyzing and Predicting Privacy Settings in the Social Web | | BIBAK | Full-Text | 104-117 | |
| Kaweh Djafari Naini; Ismail Sengor Altingovde; Ricardo Kawase; Eelco Herder; Claudia Niederée | |||
| Social networks provide a platform for people to connect and share
information and moments of their lives. With the increasing engagement of users
in such platforms, the volume of personal information that is exposed online
grows accordingly. Due to carelessness, unawareness or difficulties in defining
adequate privacy settings, private or sensitive information may be exposed to a
wider audience than intended or advisable, potentially with serious problems in
the private and professional life of a user. Although these causes usually
receive public attention when it involves companies' higher managing staff,
athletes, politicians or artists, the general public is also subject to these
issues. To address this problem, we envision a mechanism that can suggest users
the appropriate privacy setting for their posts taking into account their
profiles. In this paper, we present a thorough analysis of privacy settings in
Facebook posts and evaluate prediction models that can anticipate the desired
privacy settings with high accuracy, making use of the users' previous posts
and preferences. Keywords: Facebook; Privacy; Social networks | |||
| Counteracting Anchoring Effects in Group Decision Making | | BIBAK | Full-Text | 118-130 | |
| Martin Stettinger; Alexander Felfernig; Gerhard Leitner; Stefan Reiterer | |||
| Similar to single user decisions, group decisions can be affected by
decision biases. In this paper we analyze anchoring effects as a specific type
of decision bias in the context of group decision scenarios. On the basis of
the results of a user study in the domain of software requirements
prioritization we discuss results regarding the optimal time when preference
information of other users should be disclosed to the current user.
Furthermore, we show that explanations can increase the satisfaction of group
members with various aspects of a group decision process (e.g., satisfaction
with the decision and decision support quality). Keywords: Group decision making; Recommender systems; Decision biases; Anchoring
effects Note: James Chen Best Student Paper Award | |||
| Gifting as a Novel Mechanism for Personalized Museum and Gallery Interpretation | | BIBAK | Full-Text | 131-142 | |
| Lesley Fosh; Steve Benford; Boriana Koleva; Katharina Lorenz | |||
| The designers of mobile guides for museums and galleries are increasingly
concerned with delivering rich interpretation that can be personalized to meet
the diverse needs of individual visitors. However, increased personalization
can mean that the sociality of museum visits is overlooked. We present a new
approach to resolving the tension between the personal and the social that
invites visitors themselves to personalize and gift interpretations to others
in their social groups. We tested the approach in two different museum settings
and with different types of small group, to investigate how visitors
personalized experiences for one another, how the personalized experiences were
received by visitors, and how they worked as part of a social visit. We reveal
how visitors designed highly personal interpretations for one another by
drawing inspiration from both the exhibits themselves and their interpersonal
knowledge of one another. Our findings suggest that the deep level of
personalization generated by our approach can create rich, engaging and
socially coherent visits that allow visitors to achieve a balance of goals. We
conclude by discussing the broader implications of our findings for
personalization. Keywords: Museums; Galleries; Personalization; Interpretation; Collaboration | |||
| Supporting Cross-Device Web Search with Social Navigation-Based Mobile Touch Interactions | | BIBAK | Full-Text | 143-155 | |
| Shuguang Han; Daqing He; Zhen Yue; Peter Brusilovsky | |||
| The wide adoption of smartphones eliminates the time and location barriers
for people's daily information access, but also limits users' information
exploration activities due to the small mobile screen size. Thus, cross-device
web search, where people initialize information needs on one device but
complete them on another device, is frequently observed in modern search
engines, especially for exploratory information needs. This paper aims to
support the cross-device web search, on top of the commonly used
context-sensitive retrieval framework, for exploratory tasks. To better model
users' search context, our method not only utilizes the search history (query
history and click-through) but also employs the mobile touch interactions (MTI)
on mobile devices. To be more specific, we combine MTI's ability of locating
relevant subdocument content [10] with the idea of social navigation that
aggregates MTIs from other users who visit the same page. To demonstrate the
effectiveness of our proposed approach, we designed a user study to collect
cross-device web search logs on three different types of tasks from 24
participants and then compared our approach with two baselines: a traditional
full text based relevance feedback approach and a self-MTI based subdocument
relevance feedback approach. Our results show that the social navigation-based
MTIs outperformed both baselines. A further analysis shows that the performance
improvements are related to several factors, including the quality and quantity
of click-through documents, task types and users' search conditions. Keywords: Mobile touch interaction; Cross-device web search; Social navigation | |||
| A Utility Model for Tailoring Sensor Networks to Users | | BIBAK | Full-Text | 156-168 | |
| Masud Moshtaghi; Ingrid Zukerman | |||
| The proportion of people aged over 65 has significantly increased in recent
times, with further increases expected. Multiple sensor-based monitoring
solutions have been proposed to tackle the main concerns of elderly people and
their carers, viz fall detection and safe movement in the house. At the same
time, user studies have shown that cost is the most important factor when
deciding whether to install a monitoring system. In this paper, we offer a
utility-based approach for selecting a sensor configuration for a user on the
basis of his/her behaviour patterns and preferences regarding false alerts and
delay in the detection of mishaps, while taking into account his/her budget.
Our evaluation on two real-life datasets shows that our utility function
supports the selection of cost-effective sensor configurations. Keywords: Older adults; Sensor selection; Monitoring systems; Inactivity detection | |||
| Towards a Recommender Engine for Personalized Visualizations | | BIBAK | Full-Text | 169-182 | |
| Belgin Mutlu; Eduardo Veas; Christoph Trattner; Vedran Sabol | |||
| Visualizations have a distinctive advantage when dealing with the
information overload problem: since they are grounded in basic visual
cognition, many people understand them. However, creating them requires
specific expertise of the domain and underlying data to determine the right
representation. Although there are rules that help generate them, the results
are too broad to account for varying user preferences. To tackle this issue, we
propose a novel recommender system that suggests visualizations based on (i) a
set of visual cognition rules and (ii) user preferences collected in
Amazon-Mechanical Turk. The main contribution of this paper is the introduction
and the evaluation of a novel approach called VizRec that can suggest an
optimal list of top-n visualizations for heterogeneous data sources in a
personalized manner. Keywords: Personalized visualizations; Visualization recommender; Recommender systems;
Collaborative filtering; Crowd-sourcing | |||
| Cross-System Transfer of Machine Learned and Knowledge Engineered Models of Gaming the System | | BIBAK | Full-Text | 183-194 | |
| Luc Paquette; Ryan S. Baker; Adriana de Carvalho; Jaclyn Ocumpaugh | |||
| Replicable research on the behavior known as gaming the system, in which
students try to succeed by exploiting the functionalities of a learning
environment instead of learning the material, has shown it is negatively
correlated with learning outcomes. As such, many have developed models that can
automatically detect gaming behaviors, towards deploying them in online
learning environments. Both machine learning and knowledge engineering
approaches have been used to create models for a variety of software systems,
but the development of these models is often quite time consuming. In this
paper, we investigate how well different kinds of models generalize across
learning environments, specifically studying how effectively four gaming models
previously created for the Cognitive Tutor Algebra tutoring system function
when applied to data from two alternate learning environments: the scatterplot
lesson of Cognitive Tutor Middle School and ASSISTments. Our results suggest
that the similarity between the systems our model are transferred between and
the nature of the approach used to create the model impact transfer to new
systems. Keywords: Gaming the system; Cognitive tutors; ASSISTments; Machine learning;
Cognitive modeling; Cross-system transfer | |||
| MobiScore: Towards Universal Credit Scoring from Mobile Phone Data | | BIBA | Full-Text | 195-207 | |
| Jose San Pedro; Davide Proserpio; Nuria Oliver | |||
| Credit is a widely used tool to finance personal and corporate projects. The risk of default has motivated lenders to use a credit scoring system, which helps them make more efficient decisions about whom to extend credit. Credit scores serve as a financial user model, and have been traditionally computed from the user's past financial history. As a result, people without any prior financial history might be excluded from the credit system. In this paper we present MobiScore, an approach to build a model of the user's financial risk from mobile phone usage data, which previous work has shown to convey information about e.g. personality and socioeconomic status. MobiScore could replace traditional credit scores when no financial history is available, providing credit access to currently excluded population sectors, or be used as a complementary source of information to improve traditional finance-based scores. We validate the proposed approach using real data from a telecommunications operator and a financial institution in a Latin American country, resulting in an accurate model of default comparable to traditional credit scoring techniques. | |||
| Where to Next? A Comparison of Recommendation Strategies for Navigating a Learning Object Repository | | BIBAK | Full-Text | 208-215 | |
| Jennifer Sabourin; Lucy Kosturko; Scott McQuiggan | |||
| This paper explores the initial investigation of six recommendation
algorithms for deployment in SAS® Curriculum Pathways®, an online
repository which houses over 1250 educational resources. The proposed
approaches stem from three basic strategies: recommendations based on resource
metadata, user behavior, and alignment to academic standards. An evaluation
from subject experts suggests that usage-based recommendations are best aligned
with teacher needs, though there are interesting domain interactions that
suggest the need for continued investigation. Keywords: Recommender systems; Learning object repository; Technology enhanced
learning | |||
| Diagrammatic Student Models: Modeling Student Drawing Performance with Deep Learning | | BIBAK | Full-Text | 216-227 | |
| Andy Smith; Wookhee Min; Bradford W. Mott; James C. Lester | |||
| Recent years have seen a growing interest in the role that student drawing
can play in learning. Because drawing has been shown to contribute to students'
learning and increase their engagement, developing student models to
dynamically support drawing holds significant promise. To this end, we
introduce diagrammatic student models, which reason about students' drawing
trajectories to generate a series of predictions about their conceptual
knowledge based on their evolving sketches. The diagrammatic student modeling
framework utilizes deep learning, a family of machine learning methods based on
a deep neural network architecture, to reason about sequences of student
drawing actions encoded with temporal and topological features. An evaluation
of the deep-learning-based diagrammatic student models suggests that it can
predict student performance more accurately and earlier than competitive
baseline approaches. Keywords: Student modeling; Intelligent tutoring systems; Deep learning | |||
| Lessons Learned -- 15 Years of Delivering Real-World Pedagogically Driven Courses Using Experimental Platforms | | BIBAK | Full-Text | 228-239 | |
| Athanasios Staikopoulos; Ian O'Keeffe; Owen Conlan | |||
| Advancing research and developing innovative personalization platform whilst
at the same time striving to evolve and improve learning experiences for
learners, is a challenging prospect. This may be achieved through a
longitudinal commitment to developing real-world educational offerings that are
used in the daily delivery of learning experiences. This gives the opportunity
to continuously apply methods, develop platforms and perform evaluations to
realize improvements in the field of user modeling and personalization as well
as improving the user experiences they support. In addition, there are specific
requirements and obstacles that need to be considered like the robustness and
reliability of the experimental platform in order to make this process viable
and integrated both with the daily tasks of users and the core business
activities of the institution. In this paper, we record our experiences in
delivering real-world online courses using experimental platforms that advance
personalization and user modeling techniques for over 15 years. We also
describe how our research and technology, which is driven by solid pedagogical
requirements, has evolved during that time in order to deliver richer learning
experiences. Keywords: Personalization platforms; Challenges using experimental platforms;
Improving learning experiences; Pedagogical driven courses; User modelling | |||
| Smartphone Based Stress Prediction | | BIBAK | Full-Text | 240-251 | |
| Thomas Stütz; Thomas Kowar; Michael Kager; Martin Tiefengrabner; Markus Stuppner; Jens Blechert; Frank H. Wilhelm; Simon Ginzinger | |||
| Smartphone usage has tremendously increased and most users keep their
smartphones close throughout the day. Smartphones have a broad variety of
sensors, that could automatically map and track the user's life and behaviour.
In this work we investigate whether automatically collected smartphone usage
and sensor data can be employed to predict the experienced stress levels of a
user using a customized brief version of the Perceived Stress Scale (PSS). To
that end we have conducted a user study in which smartphone data and stress (as
measured by the PSS seven times a day) were recorded for two weeks. We found
significant correlations between stress scores and smartphone usage as well as
sensor data, pointing to innovative ways for automatic stress measurements via
smartphone technology. Stress is a prevalent risk factor for multiple diseases.
Thus accurate and efficient prediction of stress levels could provide means for
targeted prevention and intervention. Keywords: Stress; Prediction; Smartphone sensing; Data analysis; Field study;
Observational study | |||
| Exploiting Implicit Item Relationships for Recommender Systems | | BIBA | Full-Text | 252-264 | |
| Zhu Sun; Guibing Guo; Jie Zhang | |||
| Collaborative filtering inherently suffers from the data sparsity and cold start problems. Social networks have been shown useful to help alleviate these issues. However, social connections may not be available in many real systems, whereas implicit item relationships are lack of study. In this paper, we propose a novel matrix factorization model by taking into account implicit item relationships. Specifically, we employ an adapted association rule technique to reveal implicit item relationships in terms of item-to-item and group-to-item associations, which are then used to regularize the generation of low-rank user- and item-feature matrices. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach against other counterparts. | |||
| The Mars and Venus Effect: The Influence of User Gender on the Effectiveness of Adaptive Task Support | | BIBAK | Full-Text | 265-276 | |
| Alexandria Katarina Vail; Kristy Elizabeth Boyer; Eric N. Wiebe; James C. Lester | |||
| Providing adaptive support to users engaged in learning tasks is the central
focus of intelligent tutoring systems. There is evidence that female and male
users may benefit differently from adaptive support, yet it is not understood
how to most effectively adapt task support to gender. This paper reports on a
study with four versions of an intelligent tutoring system for introductory
computer programming offering different levels of cognitive (conceptual and
problem-solving) and affective (motivational and engagement) support. The
results show that female users reported significantly more engagement and less
frustration with the affective support system than with other versions. In a
human tutorial dialogue condition used for comparison, a consistent difference
was observed between females and males. These results suggest the presence of
the Mars and Venus Effect, a systematic difference in how female and male users
benefit from cognitive and affective adaptive support. The findings point
toward design principles to guide the development of gender-adaptive
intelligent tutoring systems. Keywords: Gender effects; Adaptive support; Intelligent tutoring systems; Affect;
Engagement; Frustration Note: Springer Best Paper Award | |||
| Quiet Eye Affects Action Detection from Gaze More Than Context Length | | BIBAK | Full-Text | 277-288 | |
| Hana Vrzakova; Roman Bednarik | |||
| Every purposive interactive action begins with an intention to interact. In
the domain of intelligent adaptive systems, behavioral signals linked to the
actions are of great importance, and even though humans are good in such
predictions, interactive systems are still falling behind. We explored mouse
interaction and related eye-movement data from interactive problem solving
situations and isolated sequences with high probability of interactive action.
To establish whether one can predict the interactive action from gaze, we 1)
analyzed gaze data using sliding fixation sequences of increasing length and 2)
considered sequences several fixations prior to the action, either containing
the last fixation before action (i.e. the quiet eye fixation) or not. Each
fixation sequence was characterized by 54 gaze features and evaluated by an
SVM-RBF classifier. The results of the systematic evaluation revealed
importance of the quiet eye fixation and statistical differences of quiet eye
fixation compared to other fixations prior to the action. Keywords: Action; Intentions; Prediction; Eye-tracking; SVM; Mouse interaction;
Problem solving | |||
| User Model in a Box: Cross-System User Model Transfer for Resolving Cold Start Problems | | BIBAK | Full-Text | 289-301 | |
| Chirayu Wongchokprasitti; Jaakko Peltonen; Tuukka Ruotsalo; Payel Bandyopadhyay; Giulio Jacucci; Peter Brusilovsky | |||
| Recommender systems face difficulty in cold-start scenarios where a new user
has provided only few ratings. Improving cold-start performance is of great
interest. At the same time, the growing number of adaptive systems makes it
ever more likely that a new user in one system has already been a user in
another system in related domains. To what extent can a user model built by one
adaptive system help address a cold start problem in another system? We compare
methods of cross-system user model transfer across two large real-life systems:
we transfer user models built for information seeking of scientific articles in
the SciNet exploratory search system, operating over tens of millions of
articles, to perform cold-start recommendation of scientific talks in the CoMeT
talk management system, operating over hundreds of talks. Our user study
focuses on transfer of novel explicit open user models curated by the user
during information seeking. Results show strong improvement in cold-start talk
recommendation by transferring open user models, and also reveal why explicit
open models work better in cross-domain context than traditional hidden
implicit models. Keywords: Cross-system user modeling; Recommender systems | |||
| Implicit Acquisition of User Personality for Augmenting Movie Recommendations | | BIBAK | Full-Text | 302-314 | |
| Wen Wu; Li Chen | |||
| In recent years, user personality has been recognized as valuable info to
build more personalized recommender systems. However, the effort of explicitly
acquiring users' personality traits via psychological questionnaire is
unavoidably high, which may impede the application of personality-based
recommenders in real life. In this paper, we focus on deriving users'
personality from their implicit behavior in movie domain and hence enabling the
generation of recommendations without involving users' efforts. Concretely, we
identify a set of behavioral features through experimental validation, and
develop inference model based on Gaussian Process to unify these features for
determining users' big-five personality traits. We then test the model in a
collaborative filtering based recommending framework on two real-life movie
datasets, which demonstrates that our implicit personality based recommending
algorithm significantly outperforms related methods in terms of both rating
prediction and ranking accuracy. The experimental results point out an
effective solution to boost the applicability of personality-based recommender
systems in online environment. Keywords: Recommender systems; User personality; Implicit acquisition; Collaborative
filtering Note: James Chen Best Student Paper Award | |||
| Harnessing Engagement for Knowledge Creation Acceleration in Collaborative Q&A Systems | | BIBA | Full-Text | 315-327 | |
| Jie Yang; Alessandro Bozzon; Geert-Jan Houben | |||
| Thanks to reputation and gamification mechanisms, collaborative question answering systems coordinate the process of topical knowledge creation of thousands of users. While successful, these systems face many challenges: on one hand, the volume of submitted questions overgrows the amount of new users willing, and capable, of answering them. On the other hand, existing users need to be retained and optimally allocated. Previous work demonstrates the positive effects that two important aspects, namely engagement and expertise valorisation, can have on user quality and quantity of participation. The magnitude of their effect can greatly vary across users and across topics. In this paper we advocate for a more in-depth study of the interplay that exists between user engagement factors in question answering systems. Our working hypothesis is that the process of knowledge creation can be accelerated by better understanding and exploiting the combined effects of the interests and expertise of users, with their intrinsic and extrinsic motivations. We perform a study over 6 years of data from the StackOverflow platform. By defining metrics of expertise and (intrinsic and extrinsic) motivations, we show how they distribute and correlate across platform's users and topics. By means of an off-line question routing experiment, we show how topic-specific combinations of motivations and expertise can help accelerating the knowledge creation process. | |||
| News Recommender Based on Rich Feedback | | BIBAK | Full-Text | 331-336 | |
| Liliana Ardissono; Giovanna Petrone; Francesco Vigliaturo | |||
| This paper proposes to exploit author-defined tags and social interaction
data (commenting and sharing news items) in news recommendation. Moreover it
presents a hybrid news recommender which suggest news items on the basis of the
reader's short and long-term reading history, taking reading trends and
short-term interests into account. The experimental results we carried out
provided encouraging results about the accuracy of the recommendations. Keywords: Hybrid news recommender; Tag-based news specification | |||
| News Recommenders: Real-Time, Real-Life Experiences | | BIBAK | Full-Text | 337-342 | |
| Doychin Doychev; Rachael Rafter; Aonghus Lawlor; Barry Smyth | |||
| In this paper we share our experiences of working with a real-time news
recommendation framework with real-world user and data. Keywords: Real-life experiences; News Recommender Systems | |||
| On the Use of Cross-Domain User Preferences and Personality Traits in Collaborative Filtering | | BIBAK | Full-Text | 343-349 | |
| Ignacio Fernández-Tobías; Iván Cantador | |||
| We present a study comparing collaborative filtering methods enhanced with
user personality traits and cross-domain ratings in multiple domains on a
relatively large dataset. We show that incorporating additional ratings from
source domains allows improving the accuracy of recommendations in a different
target domain, and that in certain cases, it is better to enrich user models
with both cross-domain ratings and personality trait information. Keywords: Collaborative filtering; Personality; Cross-domain recommendation | |||
| Understanding Real-Life Website Adaptations by Investigating the Relations Between User Behavior and User Experience | | BIBAK | Full-Text | 350-356 | |
| Mark P. Graus; Martijn C. Willemsen; Kevin Swelsen | |||
| We study how a website adaptation based on segment predictions from click
streams affects visitor behavior and user experience. Through statistical
analysis we investigate how the adaptation changed actual behavior. Through
structural equation modeling of subjective experience we answer why the change
in behavior occurred. The study shows the value of using survey data for
constructing and evaluating predictive models. It additionally shows how a
website adaptation influences user experience and how this in turn influences
visitor behavior. Keywords: Online adaptation; Visitor behavior; User experience; Online behavior;
Online segmentation; Structural equation modeling | |||
| Modelling the User Modelling Community (and Other Communities as Well) | | BIBA | Full-Text | 357-363 | |
| Dario De Nart; Dante Degl'Innocenti; Andrea Pavan; Marco Basaldella; Carlo Tasso | |||
| Discovering and modelling research communities' activities is a task that can lead to a more effective scientific process and support the development of new technologies. Journals and conferences already offer an implicit clusterization of researchers and research topics, and social analysis techniques based on co-authorship relations can highlight hidden relationships among researchers, however, little work has been done on the actual content of publications. We claim that a content-based analysis on the full text of accepted papers may lead to a better modelling and understanding of communities' activities and their emerging trends. In this work we present an extensive case study of research community modelling based upon the analysis of over 450 events and 7000 papers. | |||
| Personality Correlates for Digital Concert Program Notes | | BIBAK | Full-Text | 364-369 | |
| Marko Tkalcic; Bruce Ferwerda; David Hauger; Markus Schedl | |||
| In classical music concerts, the concert program notes are distributed to
the audience in order to provide background information on the composer, piece
and performer. So far, these have been printed documents composed mostly of
text. With some delay, mobile devices are making their way also in the world of
classical concerts, hence offering additional options for digital program notes
comprising not only text but also images, video and audio. Furthermore, these
digital program notes can be personalized. In this paper, we present the
results of a user study that relates personal characteristics (personality and
background musical knowledge) to preferences for digital program notes. Keywords: Classical music; Digital program notes; Personality | |||
| Integrating Context Similarity with Sparse Linear Recommendation Model | | BIBAK | Full-Text | 370-376 | |
| Yong Zheng; Bamshad Mobasher; Robin Burke | |||
| Context-aware recommender systems extend traditional recommender systems by
adapting their output to users' specific contextual situations. Most of the
existing approaches to context-aware recommendation involve directly
incorporating context into standard recommendation algorithms (e.g.,
collaborative filtering, matrix factorization). In this paper, we highlight the
importance of context similarity and make the attempt to incorporate it into
context-aware recommender. The underlying assumption behind is that the
recommendation lists should be similar if their contextual situations are
similar. We integrate context similarity with sparse linear recommendation
model to build a similarity-learning model. Our experimental evaluation
demonstrates that the proposed model is able to outperform several
state-of-the-art context-aware recommendation algorithms for the top-N
recommendation task. Keywords: Context; Context-aware recommendation; Context similarity | |||
| Modeling Motivational States Through Interpreting Physical Activity Data for Adaptive Robot Companions | | BIBA | Full-Text | 379-384 | |
| Elena Corina Grigore | |||
| This research aims to develop an adaptive human-robot interaction system that works with users over long periods of time to achieve a common goal that is beneficial to the user. The particular scenario I focus on is that of a robot companion interacting with adolescents, helping them succeed at achieving daily physical activity goals. To develop such a system, I propose a method of modeling the user's motivational state and employing this model in order to adapt motivational strategies best suited for each user. The proposed system uses both physical activity data obtained from wearable sensors (such as wristband devices) and information acquired by the robot from its interaction partners. | |||
| Privacy-Enhanced Personalisation of Web Search | | BIBAK | Full-Text | 385-390 | |
| Anisha T. J. Fernando | |||
| In personalised search, user information needs captured through cookies and
Web search history for example, make it possible to infer personal or sensitive
information about a person. Although prior studies have established sources of
privacy leakage on the Web, there is a need for identifying the sources of data
leakage concerning personalised search, its impact on users and on the broader
privacy laws and regulations. This research study firstly explores the
significance of attributes impacting personalised search and considers whether
the extensive collection of personal data is necessary for personalised search
results through a series of experiments measuring the impact of personalisation
and sources of data leakage. These findings will then be evaluated two-fold:
through a qualitative study of users, and assessed for its applicability in the
Australian context as per the Australian Privacy Principles. Further, the
outcomes from the experimental and user studies will be used to develop a
Privacy-Enhancing Technology (PET) that will provide users with options to
control personal data leakage whilst searching on the Web and enable proactive
protection of individual user privacy. Keywords: Personalisation; Web search; Data leakage; Search query parameters;
Experimental study; Privacy | |||
| From Artifact to Content Source: Using Multimodality in Video to Support Personalized Recomposition | | BIBAK | Full-Text | 391-396 | |
| Fahim A. Salim | |||
| Video content is being produced in ever increasing quantities. It is
practically impossible for any user to see every piece of video which could be
useful to them. We need to look at video content differently. Videos are
composed of a set of features, namely the moving video track, the audio track
and other derived features, such as a transcription of the spoken words. These
different features have the potential to be recomposed to create new video
offerings. However, a key step in achieving such recomposition is the
appropriate decomposition of those features into useful assets. Video artifacts
can therefore be considered a type of multimodal source which may be used to
support personalized and contextually aware recomposition. This work aims to
propose and validate an approach which will convert a video from a single
artifact into a diverse query-able content source. Keywords: Personalization; Multimodality; Video analysis; Paralinguistic; User
engagement | |||
| Exploiting Item and User Relationships for Recommender Systems | | BIBA | Full-Text | 397-402 | |
| Zhu Sun | |||
| Recommender systems have become a prevalent tool to cope with the information overload problem. The most well-known recommendation technique is collaborative filtering (CF), whereby a user's preference can be predicted by her like-minded users. Data sparsity and cold start are two inherent and severe limitations of CF. | |||