| Opinion-Driven Matrix Factorization for Rating Prediction | | BIBAK | Full-Text | 1-13 | |
| Štefan Pero; Tomáš Horváth | |||
| Rating prediction is a well-known recommendation task aiming to predict a
user's rating for those items which were not rated yet by her. Predictions are
computed from users' explicit feedback, i.e. their ratings provided on some
items in the past. Another type of feedback are user reviews provided on items
which implicitly express users' opinions on items. Recent studies indicate that
opinions inferred from users' reviews on items are strong predictors of user's
implicit feedback or even ratings and thus, should be utilized in computation.
As far as we know, all the recent works on recommendation techniques utilizing
opinions inferred from users' reviews are either focused on the item
recommendation task or use only the opinion information, completely leaving
users' ratings out of consideration. The approach proposed in this paper is
filling this gap, providing a simple, personalized and scalable rating
prediction framework utilizing both ratings provided by users and opinions
inferred from their reviews. Experimental results provided on a dataset
containing user ratings and reviews from the real-world Amazon Product Review
Data show the effectiveness of the proposed framework. Keywords: rating prediction; opinion mining; recommendation; personalization | |||
| Interaction Based Content Recommendation in Online Communities | | BIBA | Full-Text | 14-24 | |
| Surya Nepal; Cécile Paris; Payam Aghaei Pour; Jill Freyne; Sanat Kumar Bista | |||
| Content recommender systems have become an invaluable tools in online communities where a huge volume of content items are generated for users to consume, making it difficult for users to find interesting content. Many recommender systems leverage articulated social networks or profile information (e.g, user background, interest, etc.) for content recommendation. These recommenders largely ignore the implied networks defined through user interactions. Yet these play an important role in formulating users' common interests. We propose an interaction based content recommender which leverages implicit user interactions to determine the relationship trust or strength, generating a richer, more informed implied network. An offline analysis on a 5000 person, 12 week dataset from an online community shows that our approach outperforms algorithms which focus on articulated networks that do not consider relationship trust or strength. | |||
| What Recommenders Recommend -- An Analysis of Accuracy, Popularity, and Sales Diversity Effects | | BIBA | Full-Text | 25-37 | |
| Dietmar Jannach; Lukas Lerche; Fatih Gedikli; Geoffray Bonnin | |||
| In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other business-oriented metrics such as click-through-rates, customer retention or effects on the sales spectrum might be the true evaluation criteria for RS effectiveness. In this paper, we compare different RS algorithms with respect to their tendency of focusing on certain parts of the product spectrum. Our first analysis on different data sets shows that some algorithms -- while able to generate highly accurate predictions -- concentrate their top 10 recommendations on a very small fraction of the product catalog or have a strong bias to recommending only relatively popular items than others. We see our work as a further step toward multiple-metric offline evaluation and to help service providers make better-informed decisions when looking for a recommendation strategy that is in line with the overall goals of the recommendation service. | |||
| A Framework for Trust-Based Multidisciplinary Team Recommendation | | BIBA | Full-Text | 38-50 | |
| Lorenzo Bossi; Stefano Braghin; Anwitaman Datta; Alberto Trombetta | |||
| Often one needs to form teams in order to perform a complex collaborative task. Therefore, it is interesting and useful to assess how well constituents of a team have performed, and leverage this knowledge to guide future team formation. In this work we propose a model for assessing the reputation of participants in collaborative teams. The model takes into account several features such as the different skills that a participant has and the feedback of team participants on her/his previous works. We validate our model based on synthetic datasets extrapolated from real-life scenarios. | |||
| Semantic Aggregation and Zooming of User Viewpoints in Social Media Content | | BIBAK | Full-Text | 51-63 | |
| Dimoklis Despotakis; Vania Dimitrova; Lydia Lau; Dhavalkumar Thakker | |||
| Social web provides rich content for gaining an understanding about the
users which can empower adaptation. There is a current trend to extract user
profiles from social media content using semantic augmentation and linking to
domain ontologies. The paper shows a further step in this research strand,
exploiting semantics to get a deeper understanding about the users by
extracting the domain regions where the users focus, which are defined as
viewpoints. The paper outlines a formal framework for extracting viewpoints
from semantic tags associated with user comments. This enables zooming into the
viewpoints at different aggregation layers, as well as comparing users on the
basis of the areas where they focus. The framework is applied on YouTube
content, illustrating an insight into emotions users refer to in their comments
on job interview videos. Keywords: Social media content; User model representation and extraction; Viewpoints;
YouTube; Adaptive learning | |||
| Learning Likely Locations | | BIBAK | Full-Text | 64-76 | |
| John Krumm; Rich Caruana; Scott Counts | |||
| We show that people's travel destinations are predictable based on simple
features of their home and destination. Using geotagged Twitter data from over
200,000 people in the U.S., with a median of 10 visits per user, we use machine
learning to classify whether or not a person will visit a given location. We
find that travel distance is the most important predictive feature. Ignoring
distance, using only demographic features pertaining to race, age, income, land
area, and household density, we can predict travel destinations with 84%
accuracy. We present a careful analysis of the power of individual and grouped
demographic features to show which ones have the most predictive impact for
where people go. Keywords: Human mobility; location prediction; Twitter | |||
| Scrutable User Models and Personalised Item Recommendation in Mobile Lifestyle Applications | | BIBAK | Full-Text | 77-88 | |
| Rainer Wasinger; James Wallbank; Luiz Pizzato; Judy Kay; Bob Kummerfeld; Matthias Böhmer; Antonio Krüger | |||
| This paper presents our work on supporting scrutable user models for use in
mobile applications that provide personalised item recommendations. In
particular, we describe a mobile lifestyle application in the fine-dining
domain, designed to recommend meals at a particular restaurant based on a
person's user model. The contributions of this work are three-fold. First is
the mobile application and its personalisation engine for item recommendation
using a content and critique-based hybrid recommender. Second, we illustrate
the control and scrutability that a user has in configuring their user model
and browsing a content list. Thirdly, this is validated in a user experiment
that illustrates how new digital features may revolutionise the way that
paper-based systems (like restaurant menus) currently work. Although this work
is based on restaurant menu recommendations, its approach to scrutability and
mobile client-side personalisation carry across to a broad class of commercial
applications. Keywords: Mobile personalisation; user modelling; scrutability; recommender technology | |||
| Days of Our Lives: Assessing Day Similarity from Location Traces | | BIBAK | Full-Text | 89-101 | |
| James Biagioni; John Krumm | |||
| We develop and test algorithms for assessing the similarity of a person's
days based on location traces recorded from GPS. An accurate similarity measure
could be used to find anomalous behavior, to cluster similar days, and to
predict future travel. We gathered an average of 46 days of GPS traces from 30
volunteer subjects. Each subject was shown random pairs of days and asked to
assess their similarity. We tested eight different similarity algorithms in an
effort to accurately reproduce our subjects' assessments, and our statistical
tests found two algorithms that performed better than the rest. We also
successfully applied one of our similarity algorithms to clustering days using
location traces. Keywords: location traces; similarity; anomaly detection; clustering | |||
| Studying the Effect of Human Cognition on User Authentication Tasks | | BIBAK | Full-Text | 102-113 | |
| Marios Belk; Panagiotis Germanakos; Christos Fidas; George Samaras | |||
| This paper studies the effect of individual differences in human cognition
on user performance in authentication tasks. In particular, a text-based
password and a recognition-based graphical authentication mechanism were
deployed in the frame of an ecological valid experimental design, to
investigate the effect of individuals' different cognitive processing abilities
toward efficiency and effectiveness of user authentication tasks. A total of
107 users participated in the reported study during a three-month period
between September and November 2012. The results of this recent study can be
interpreted under the light of human information processing as they demonstrate
a main effect of users' cognitive processing abilities on both efficiency and
effectiveness related to authentication mechanisms. The main findings can be
considered valuable for future deployment of adaptive security mechanisms since
it has been initially shown that specific cognitive characteristics of users
could be a determinant factor for the adaptation of security mechanisms. Keywords: Individual Differences; Cognitive Processing Characteristics; User
Authentication; Efficiency; Effectiveness; User Study | |||
| Modeling a Graph Viewer's Effort in Recognizing Messages Conveyed by Grouped Bar Charts | | BIBA | Full-Text | 114-126 | |
| Richard Burns; Sandra Carberry; Stephanie Elzer Schwartz | |||
| Information graphics (bar charts, line graphs, etc.) in popular media generally have a high-level message that they are intended to convey. These messages are seldom repeated in the document's text yet contribute to understanding the overall document. The relative perceptual effort required to recognize a particular message is a communicative signal that serves as a clue about whether that message is the one intended by the graph designer. This paper presents a model of relative effort by a viewer for recognizing different messages from grouped bar charts. The model is implemented within the ACT-R cognitive framework and has been validated by human subjects experiments. We also present a statistical analysis of the contribution of effort in recognizing the intended message of a grouped bar chart. | |||
| Evaluation of Attention Levels in a Tetris Game Using a Brain Computer Interface | | BIBAK | Full-Text | 127-138 | |
| Georgios Patsis; Hichem Sahli; Werner Verhelst; Olga De Troyer | |||
| This paper investigates the possibility of using information from brain
signals, obtained through a light and inexpensive Brain Computer Interface
(BCI), in order to dynamically adjust the difficulty of an educational video
game and adapt the level of challenge to players' abilities. In this
experiment, attention levels of Tetris players -- measured with the BCI -- have
been evaluated as a function of game difficulty. Processing of the data
revealed that both in intra- and inter- player analysis, an increase in game
difficulty was followed by an increase in attention. These results come in
accordance with similar experiments performed with a 19 sensor EEG cap, as
opposed to the single-dry-sensor BCI used here. These findings give new
possibilities in the development of educational games that adapt to the mental
state of player/learner. Keywords: brain signal; brain computer interface; attention levels; Tetris;
dynamically adjust game difficulty | |||
| Monitoring Personal Safety by Unobtrusively Detecting Unusual Periods of Inactivity | | BIBAK | Full-Text | 139-151 | |
| Masud Moshtaghi; Ingrid Zukerman; David Albrecht; R. Andrew Russell | |||
| Due to the ageing of the world population, a growing number of elderly
people remain in their homes, requiring different levels of care. Our formative
user studies show that the main concern of elderly people and their families is
"fall detection and safe movement in the house", while eschewing intrusive
monitoring devices. This paper introduces a statistical model based on
non-intrusive sensor observations that posits whether a person is not safe by
identifying unusually long periods of inactivity within different regions in
the home. Evaluation on two real-life datasets shows that our system
outperforms a state-of-the-art system. Keywords: Older adults; sensors; inactivity detection; statistical model | |||
| Recommendation with Differential Context Weighting | | BIBAK | Full-Text | 152-164 | |
| Yong Zheng; Robin Burke; Bamshad Mobasher | |||
| Context-aware recommender systems (CARS) adapt their recommendations to
users' specific situations. In many recommender systems, particularly those
based on collaborative filtering, the contextual constraints may lead to
sparsity: fewer matches between the current user context and previous
situations. Our earlier work proposed an approach called differential context
relaxation (DCR), in which different subsets of contextual features were
applied in different components of a recommendation algorithm. In this paper,
we expand on our previous work on DCR, proposing a more general approach --
differential context weighting (DCW), in which contextual features are
weighted. We compare DCR and DCW on two real-world datasets, and DCW
demonstrates improved accuracy over DCR with comparable coverage. We also show
that particle swarm optimization (PSO) can be used to efficiently determine the
weights for DCW. Keywords: recommender systems; collaborative filtering; context; context-aware
recommendation | |||
| Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation | | BIBAK | Full-Text | 165-177 | |
| Victor Codina; Francesco Ricci; Luigi Ceccaroni | |||
| Context-aware recommender systems aim at outperforming traditional
context-free recommenders by exploiting information about the context under
which the users' ratings are acquired. In this paper we present a novel
contextual pre-filtering approach that takes advantage of the semantic
similarities between contextual situations. For assessing context similarity we
rely only on the available users' ratings and we deem as similar two contextual
situations that are influencing in a similar way the user's rating behavior. We
present an extensive comparative evaluation of the proposed approach using
several contextually-tagged ratings data sets. We show that it outperforms
state-of-the-art context-aware recommendation techniques. Keywords: Recommenders; Implicit Semantics; Collaborative Filtering; Matrix
Factorization | |||
| Combining Collaborative Filtering and Text Similarity for Expert Profile Recommendations in Social Websites | | BIBAK | Full-Text | 178-189 | |
| Alexandre Spaeth; Michel C. Desmarais | |||
| People-to-people recommendation differ from item recommendations in a number
of ways, one of which is that individuals add information to their profile
which is often critical in determining a good match. The most critical
information can be in the form of free text or personal tags. We explore
text-mining techniques to improve classical collaborative filtering methods for
a site aimed at matching people who are looking for expert advice on a specific
topic. We compare results from a LSA-based text similarity analysis, a simple
user-user collaborative filter, and a combination of both methods used to
recommend people to meet for a knowledge-sharing website. Evaluations show that
LSA similarity has a better precision at low recall rates, whereas
collaborative filters have a better precision at higher recall rates. A
combination of both can outperform the results of the simpler algorithms. Keywords: Social Recommender Systems; Text Mining; People Recommendation;
Content-based Recommender; Collaborative Filtering | |||
| Adapting Recommendation Diversity to Openness to Experience: A Study of Human Behaviour | | BIBAK | Full-Text | 190-202 | |
| Nava Tintarev; Matt Dennis; Judith Masthoff | |||
| This paper uses a User-as-Wizard approach to evaluate how people apply
diversity to a set of recommendations. In particular, it considers how
diversity is applied for a recipient with high or low Openness to Experience, a
personality trait from the Five Factor Model. While there was no effect of the
personality trait on the degree of diversity applied, there seems to be a trend
in the way in which it was applied. Maximal categorical diversity (across
genres) was more likely to be applied to those with high Openness to
Experience, at the expense of maximal thematic diversity (within genres). Keywords: Diversity; Serendipity; Personality; Recommender Systems | |||
| Predicting Successful Inquiry Learning in a Virtual Performance Assessment for Science | | BIBAK | Full-Text | 203-214 | |
| Ryan S. J. D. Baker; Jody Clarke-Midura | |||
| In recent years, models of student inquiry skill have been developed for
relatively tightly-scaffolded science simulations. However, there is an
increased interest in researching how video games and virtual environments can
be used for both learning and assessment of science inquiry skills and
practices. Such environments allow students to explore scientific content in a
more open-ended context that is designed around actions and choices. In such an
environment, students move an avatar around a world, speak to in-game
characters, obtain objects, and take those objects to laboratories to run
specific tests. While these environments allow for more autonomy and choice,
assessing skills in these environments is a more difficult challenge than in
closed environments or simulations. In this paper, we present models that can
infer two aspects of middle-school students' inquiry skill, from their
interactive behaviors within an assessment in a virtual environment called a
"virtual performance assessment" or VPA: 1) whether the student successfully
demonstrates the skill of designing controlled experiments within the VPA, and
2) whether a middle-school student can successfully use their inquiry skill to
determine the answer to a scientific question with a non-intuitive in-game
answer. Keywords: student modeling; skill modeling; inquiry learning; virtual performance
assessment | |||
| Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation | | BIBAK | Full-Text | 215-227 | |
| Samad Kardan; Cristina Conati | |||
| This paper presents an experimental evaluation of eye gaze data as a source
for modeling user's learning in Interactive Simulations (IS). We compare the
performance of classifier user models trained only on gaze data vs. models
trained only on interface actions vs. models trained on the combination of
these two sources of user interaction data. Our long-term goal is to build user
models that can trigger adaptive support for students who do not learn well
with ISs, caused by the often unstructured and open-ended nature of these
environments. The test-bed for our work is the CSP applet, an IS for Constraint
Satisfaction Problems (CSP). Our findings show that including gaze data as an
additional source of information to the CSP applet's user model significantly
improves model accuracy compared to using interface actions or gaze data alone. Keywords: Eye tacking; Eye Movement Data; Interface Actions; Interactive Simulations;
User Classification; Clustering; Data Mining for User Modeling | |||
| Utilizing Dynamic Bayes Nets to Improve Early Prediction Models of Self-regulated Learning | | BIBAK | Full-Text | 228-241 | |
| Jennifer Sabourin; Bradford Mott; James Lester | |||
| Student engagement and motivation during learning activities is tied to
better learning behaviors and outcomes and has prompted the development of
learner-guided environments. These systems attempt to personalize learning by
allowing students to select their own tasks and activities. However, recent
evidence suggests that not all students are equally capable of guiding their
own learning. Some students are highly self-regulated learners and are able to
select learning goals, identify appropriate tasks and activities to achieve
these goals and monitor their progress resulting in improved learning and
motivational benefits over traditional learning tasks. Students who lack these
skills are markedly less successful in self-guided learning environments and
require additional scaffolding to be able to navigate them successfully. Prior
work has examined these phenomena within the learner-guided environment,
Crystal Island, and identified the need for early prediction of students'
self-regulated learning abilities. This work builds upon these findings and
presents a dynamic Bayesian approach that significantly improves the
classification accuracy of student self-regulated learning skills. Keywords: Student modeling; intelligent tutoring systems; self-regulated learning | |||
| Recommending Topics for Web Curation | | BIBAK | Full-Text | 242-253 | |
| Zurina Saaya; Markus Schaal; Rachael Rafter; Barry Smyth | |||
| A new generation of curation services provides users with a set of tools to
manually curate and manage topical collections of content. However, given
curation is ultimately a manual effort, it still requires significant effort on
the part of the curator both in terms of collecting and managing content. We
are interested in providing additional assistance to users in their curation
tasks, in particular when it comes to efficiently adding content to their
collection, and examine recommender systems in an effort to automate this task.
We examine a number of recommendation strategies using live-user data from the
popular Scoop.it curation service. Keywords: curation; recommendation system; machine learning | |||
| Building Rich User Search Queries Profiles | | BIBAK | Full-Text | 254-266 | |
| Elif Aktolga; Alpa Jain; Emre Velipasaoglu | |||
| It is well-known that for a variety of search tasks involving queries more
relevant results can be presented if they are personalized according to a
user's interests and search behavior. This can be achieved with user-dependent,
rich web search queries profiles. These are typically built as part of a
specific search personalization task so that it is unclear which
characteristics of queries are most effective for modeling the user-query
relationship in general. In this paper, we explore various approaches for
explicitly modeling this user-query relationship independently of other search
components. Our models employ generative models in layers in a prediction task.
The results show that the best signals for modeling the user-query relationship
come from the given query's terms and entities together with information from
related entities and terms, yielding a relative improvement of up to 24.5% in
MRR and Success over the baseline methods. Keywords: User Profiles; Personalization; Named Entities | |||
| Inform or Flood: Estimating When Retweets Duplicate | | BIBA | Full-Text | 267-273 | |
| Amit Tiroshi; Tsvi Kuflik; Shlomo Berkovsky | |||
| The social graphs of Twitter users often overlap, such that retweets may cause duplicate posts is a user's incoming stream of tweets. Hence, it is important for the retweets to strike the balance between sharing information and flooding the recipients with redundant tweets. In this work, we present an exploratory analysis that assesses the degree of duplication caused by a set of real retweets. The results of the analysis show that although the overall duplication is not severe, high degree of duplication is caused by tweets of users with a small number of followers, which are retweeted by users with a small number of followers. We discuss the limitations of this work and propose several enhancements that we intend to pursue in the future. | |||
| Predicting Users' Preference from Tag Relevance | | BIBAK | Full-Text | 274-280 | |
| Tien T. Nguyen; John Riedl | |||
| Tagging has become a powerful means for users to find, organize, understand
and express their ideas about online entities. However, tags present great
challenges when researchers try to incorporate them into the prediction task of
recommender systems. In this paper, we propose a novel approach to infer user
preference from tag relevance, an indication of how strong each tag applies to
each item in recommender systems. We also present a methodology to choose tags
that tell most about each user's preference. Our preliminary results show that
at certain levels, some of our algorithms perform better than previous work. Keywords: algorithms; recommender system; mutual information; tag relevance | |||
| Recommendation for New Users with Partial Preferences by Integrating Product Reviews with Static Specifications | | BIBAK | Full-Text | 281-288 | |
| Feng Wang; Weike Pan; Li Chen | |||
| Recommending products to new buyers is an important problem for online
shopping services, since there are always new buyers joining a deployed system.
In some recommender systems, a new buyer will be asked to indicate her/his
preferences on some attributes of the product (like camera) in order to address
the so called cold-start problem. Such collected preferences are usually not
complete due to the user's cognitive limitation and/or unfamiliarity with the
product domain, which are called partial preferences. The fundamental challenge
of recommendation is thus that it may be difficult to accurately and reliably
find some like-minded users via collaborative filtering techniques or match
inherently preferred products with content-based methods. In this paper, we
propose to leverage some auxiliary data of online reviewers' aspect-level
opinions, so as to predict the buyer's missing preferences. The resulted user
preferences are likely to be more accurate and complete. Experiment on a real
user-study data and a crawled Amazon review data shows that our solution
achieves better recommendation performance than several baseline methods. Keywords: New users; partial preferences; product recommendation; consumer reviews;
aspect-level opinion mining; static specifications | |||
| Cross-Domain Collaborative Recommendation in a Cold-Start Context: The Impact of User Profile Size on the Quality of Recommendation | | BIBA | Full-Text | 289-295 | |
| Shaghayegh Sahebi; Peter Brusilovsky | |||
| Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multi-domain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that cross-domain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it. | |||
| Personalized Access to Scientific Publications: from Recommendation to Explanation | | BIBA | Full-Text | 296-301 | |
| Dario De Nart; Felice Ferrara; Carlo Tasso | |||
| Several recommender systems have been proposed in the literature for adaptively suggesting useful references to researchers with different interests. However, in order to access the knowledge contained in the recommended papers, the users need to read the publications for identifying the potentially interesting concepts. In this work we propose to overcome this limitation by utilizing a more semantic approach where concepts are extracted from the papers for generating and explaining the recommendations. By showing the concepts used to find the recommended articles, users can have a preliminary idea about the filtered publications, can understand the reasons why the papers were suggested and they can also provide new feedback about the relevance of the concepts utilized for generating the recommendations. | |||
| PoliSpell: An Adaptive Spellchecker and Predictor for People with Dyslexia | | BIBAK | Full-Text | 302-309 | |
| Alberto Quattrini Li; Licia Sbattella; Roberto Tedesco | |||
| People with dyslexia often face huge writing difficulties.
Spellcheckers/predictors can help, but the current systems are not appropriate
for them, because of the assumptions behind the models and because of
heavy-to-use interfaces. This paper presents a system for
spellchecking/predicting words, which can adapt both its model and its
interface according to the individual behavior. The model takes into account
typical errors made by people with dyslexia, such as boundary errors, and the
context for correcting real-word errors. The interface aims at reducing
interaction with the user. The model and the interface are easily adaptable to
general use. Keywords: spellchecker; predictor; dyslexia; adaptive system | |||
| A Framework for Privacy-Aware User Data Trading | | BIBAK | Full-Text | 310-317 | |
| Johnson Iyilade; Julita Vassileva | |||
| Data about users is rapidly growing, collected by various online
applications and databases. The ability to share user data across applications
can offer benefits to user in terms of personalized services, but at the same
time poses privacy risks of disclosure of personal information. Hence, there is
a need to ensure protection of user privacy while enabling user data sharing
for desired personalized services. We propose a policy framework for user data
sharing based on the purpose of adaptation. The framework is based on the idea
of a market, where applications can offer and negotiate user data sharing with
other applications according to an explicit user-editable and negotiable
privacy policy that defines the purpose, type of data, retention period and
price. Keywords: Privacy; Personalization; User Data Sharing; Policy; Incentives; Trust;
Market; Framework | |||
| Understanding Email Writers: Personality Prediction from Email Messages | | BIBAK | Full-Text | 318-330 | |
| Jianqiang Shen; Oliver Brdiczka; Juan Liu | |||
| Email is a ubiquitous communication tool and constitutes a significant
portion of social interactions. In this paper, we attempt to infer the
personality of users based on the content of their emails. Such inference can
enable valuable applications such as better personalization, recommendation,
and targeted advertising. Considering the private and sensitive nature of email
content, we propose a privacy-preserving approach for collecting email and
personality data. We then frame personality prediction based on the well-known
Big Five personality model and train predictors based on extracted email
features. We report prediction performance of 3 generative models with
different assumptions. Our results show that personality prediction is
feasible, and our email feature set can predict personality with reasonable
accuracies. Keywords: Personality; behavior analysis; email; text processing | |||
| Modeling Emotions with Social Tags | | BIBAK | Full-Text | 331-334 | |
| Ignacio Fernández-Tobías; Iván Cantador; Laura Plaza | |||
| We present an emotion model based on social tags, which is built upon an
automatically generated lexicon that describes emotions by means of synonym and
antonym terms. Using this model we develop a number of methods that transform
social tag-based item profiles into emotion-oriented item profiles. We show
that the model's representation of a number of basic emotions is in accordance
with the well known psychological circumplex model of affect, and we report
results from a user study that show a high precision of our methods to infer
the emotions evoked by items in the movie and music domains. Keywords: emotions; social tagging; folksonomies | |||
| Unobtrusive Monitoring of Knowledge Workers for Stress Self-regulation | | BIBAK | Full-Text | 335-337 | |
| Saskia Koldijk; Maya Sappelli; Mark Neerincx; Wessel Kraaij | |||
| In our connected workplaces it can be hard to work calm and focused. In a
simulated work environment we manipulated the stressors time pressure and email
interruptions. We found effects on subjective experience and working behavior.
Initial results indicate that the sensor data that we collected is suitable for
user state modeling in stress related terms. Keywords: Experiment; stress; knowledge worker; user state modeling | |||
| Topolor: A Social Personalized Adaptive E-Learning System | | BIBA | Full-Text | 338-340 | |
| Lei Shi; Dana Al Qudah; Alaa Qaffas; Alexandra I. Cristea | |||
| This paper briefly introduces Topolor, a social personalized adaptive e-learning system, which aims at improving fine-grained social interaction in the learning process in addition to applying classical adaptation based on user modeling. Here, we present the main features of Topolor and its preliminary evaluation that showed high system usability from a student's perspective. The intention is to demonstrate Topolor hands-on at the conference. | |||
| RES: A Personalized Filtering Tool for CiteSeerX Queries Based on Keyphrase Extraction | | BIBA | Full-Text | 341-343 | |
| Dario De Nart; Felice Ferrara; Carlo Tasso | |||
| Finding satisfactory scientific literature is still a very time-consuming task. In the last decade several tools have been proposed to approach this task, however only few of them actually analyse the whole document in order to select and present it to the user and even less tools offer any kind of explanation of why a given item was retrieved/recommended. The main goal of this demonstration is to present the RES system, a tool intended to overcome the limitations of traditional recommender and personalized information retrieval systems by exploiting a more semantic approach where concepts are extracted from the papers in order to generate and then explain the recommendation. RES acts like a personalized interface for the well-known CiteSeerX system, filtering and presenting query results accordingly to individual user's interests. | |||
| Generating a Personalized UI for the Car: A User-Adaptive Rendering Architecture | | BIBAK | Full-Text | 344-346 | |
| Michael Feld; Gerrit Meixner; Angela Mahr; Marc Seissler; Balaji Kalyanasundaram | |||
| Personalized systems are gaining popularity in various mobile scenarios. In
this work, we take on the challenges associated with the automotive domain and
present a user-adaptive graphical renderer. By supporting a strictly
model-based development processes, we meet the rigid requirements of the
industry. The proposed architecture is based on the UIML standard and a novel
rule-based adaptation framework. Keywords: Automotive; Personalization; User adaptation; UIML | |||
| Eliciting Affective Recommendations to Support Distance Learning Students | | BIBAK | Full-Text | 347-349 | |
| Ángeles Manjarrés-Riesco; Olga C. Santos; Jesus G. Boticario | |||
| Affective support can be provided through personalized recommendations
integrated within learning management systems (LMS). We have applied the TORMES
user centered engineering approach to involve educators in a recommendation
elicitation process in a distance learning (DL) context. Keywords: Educational recommender systems (ERS); DL; affective computing | |||
| Leveraging Encyclopedic Knowledge for Transparent and Serendipitous User Profiles | | BIBA | Full-Text | 350-352 | |
| Fedelucio Narducci; Cataldo Musto; Giovanni Semeraro; Pasquale Lops; Marco de Gemmis | |||
| The main contribution of this work is the comparison of different techniques for representing user preferences extracted by analyzing data gathered from social networks, with the aim of constructing more transparent (human-readable) and serendipitous user profiles. We compared two different user models representations: one based on keywords and one exploiting encyclopedic knowledge extracted from Wikipedia. A preliminary evaluation involving 51 Facebook and Twitter users has shown that the use of an encyclopedic-based representation better reflects user preferences, and helps to introduce new interesting topics. | |||
| Modelling Users' Affect in Job Interviews: Technological Demo | | BIBAK | Full-Text | 353-355 | |
| Kaska Porayska-Pomsta; Keith Anderson; Ionut Damian; Tobias Baur; Elisabeth André; Sara Bernardini; Paola Rizzo | |||
| This demo presents an approach to recognising and interpreting social
cues-based interactions in computer-enhanced job interview simulations. We show
what social cues and complex mental states of the user are relevant in this
interaction context, how they can be interpreted using static Bayesian
Networks, and how they can be recognised automatically using state-of-the-art
sensor technology in real-time. Keywords: social signal processing; complex mental states modelling; job interviews;
Bayesian inference | |||
| Multilingual vs. Monolingual User Models for Personalized Multilingual Information Retrieval | | BIBAK | Full-Text | 356-358 | |
| M. Rami Ghorab; Séamus Lawless; Alexander O'Connor; Dong Zhou; Vincent Wade | |||
| This paper demonstrates that a user of multilingual search has different
interests depending on the language used, and that the user model should
reflect this. To demonstrate this phenomenon, the paper proposes and evaluates
a set of result re-ranking algorithms based on various user model
representations. Keywords: User Modeling; Personalization; Multilingual Web Search | |||
| A Prismatic Cognitive Layout for Adapting Ontologies | | BIBAK | Full-Text | 359-362 | |
| Francesco Osborne; Alice Ruggeri | |||
| We propose a novel approach to personal ontologies, grounded on the concept
of affordance and on the ontological theory of Von Uexküll, in which each
concept can be viewed under different perspectives depending on the
subjectivity of the user and thus can yield tailored semantic relationships or
properties. We suggest a cognitive middle-layer interface between the user and
the ontology, which is able on the run to modify and adapt the ontology to the
user needs. The goal is to obtain an adapted version of the ontology that is
tailored both to the context and to the user prospective and expertise, without
the need of explicitly maintaining a high number of ontologies. Keywords: Ontology-based Recommender Systems; Personal Ontology View; Ontology
Learning; Affordance | |||
| Evaluation of Cross-Domain News Article Recommendations | | BIBA | Full-Text | 363-366 | |
| Benjamin Kille | |||
| This thesis will investigate methods to increase the utility of news article recommendation services. Access to different news providers allows us to consider cross-domain user preferences. We deal with recommender systems with continuously changing item collections. We will be able to observe user feedback from a real-world recommendation system operating on different domains. We will evaluate how results from existing data sets correspond to actual user reactions. | |||
| Suggesting Query Revisions in Conversational Recommender Systems | | BIBA | Full-Text | 367-370 | |
| Henry Blanco Lores | |||
| Recommender Systems (RS) are information tools designed to suggest items that suit users needs and preferences. They can also support users to browse a product catalogue and better understand and elicit their preferences. These activities are managed by Conversational RSs, which over a series of user-system interactions acquire and revise user preferences by observing the user reaction to proposed options. | |||
| Mining Semantic Data, User Generated Contents, and Contextual Information for Cross-Domain Recommendation | | BIBAK | Full-Text | 371-375 | |
| Ignacio Fernández-Tobías | |||
| Cross-domain recommender systems suggest items in a target domain by
exploiting user preferences and/or domain knowledge available in a source
domain. In this thesis we aim to develop a framework for cross-domain
recommendation capable of mining heterogeneous sources of information such as
semantically annotated data, user generated contents, and contextual signals.
For this purpose, we investigate a number of approaches to extract, process,
and integrate knowledge for linking distinct domains, and various models that
exploit such knowledge for making effective recommendations across domains. Keywords: Cross-domain recommender systems; semantic networks; social tagging;
context-aware recommendations | |||
| A POV-Based User Model: From Learning Preferences to Learning Personal Ontologies | | BIBAK | Full-Text | 376-379 | |
| Francesco Osborne | |||
| In recent years a variety of ontology-based recommender systems, which make
use of a domain ontology to characterize the user model, have shown to be very
effective. There are however some open issues with this approach, such as: 1)
the creation of an ontology is an expensive process; 2) the ontology seldom
takes into account the perspectives of target user communities; 3) different
groups of users may have different domain conceptualizations; 4) the ontology
is usually static and not able to learn automatically new semantic
relationships or properties. To address these points, I propose an approach to
automatically build multiple personal ontology views (POVs) from user
feedbacks, tailored to specific user groups and exploited for recommendation
purpose via spreading activation techniques. Keywords: Ontology-based User Modelling; Ontology-based Recommender Systems; Spreading
Activation; Personal Ontology; Ontology Learning | |||
| Design and Evaluation of an Affective BCI-Based Adaptive User Application: A Preliminary Case Study | | BIBAK | Full-Text | 380-383 | |
| Giuseppe Rizzo | |||
| The Brain-Computer interface (BCI) advancements made possible the use of
techniques to recognize emotional aspects from the electroencephalographic
signal (EEG). In this work I focus on the implementation of a BCI-based
application, able to mine relevant information about user's emotion from
his/her EEG signal and to adapt to it. To this aim a highly low cost and
wearable device is employed, so as, a natural interaction is allowed. Keywords: Adaptation; Affective Computing; BCI; User-Centred Evaluation | |||
| Inclusive Personalized e-Learning Based on Affective Adaptive Support | | BIBAK | Full-Text | 384-387 | |
| Sergio Salmeron-Majadas; Olga C. Santos; Jesus G. Boticario | |||
| Emotions and learning are closely related. In the PhD research presented in
this paper, that relation has to be taken advantage of. With this aim, within
the framework of affective computing, the main goal proposed is modeling
learner's affective state in order to support adaptive features and provide an
inclusive personalized e-learning experience. At the first stage of this
research, emotion detection is the principal issue to cope with. A multimodal
approach has been proposed, so gathering data from diverse sources to feed data
mining systems able to supply emotional information is being the current
ongoing work. On the next stages, the results of these data mining systems will
be used to enhance learner models and based on these, offer a better e-learning
experience to improve learner's results. Keywords: Affective Computing; Emotions; User Modeling; Human-Computer Interaction;
Data Mining; Artificial Intelligence; Multimodal Approach | |||
| Tabbed Browsing Behavior as a Source for User Modeling | | BIBAK | Full-Text | 388-391 | |
| Martin Labaj; Mária Bieliková | |||
| In our research, we focus on improving the user model by using novel sources
of user feedback -- tabbed browsing behavior of the users (also called parallel
browsing). The tabbing is nowadays established as the more accurate description
of browsing activities than the previous linear representation. Users take
advantage of multiple tabs in various scenarios, by which they express
different relations and preferences to hypermedia being visited in such tabs.
The aimed contribution is to include this behavior into the user model, so
improving accuracy of modeled user's characteristics and thus improving
personalization. Keywords: user modeling; tabbed browsing; adaptive web-based systems | |||
| Grasping the Long Tail: Personalized Search for Cultural Heritage Annotators | | BIBA | Full-Text | 392-395 | |
| Chris Dijkshoorn | |||
| Online collections of museums are often hard to access, because the artworks lack appropriate annotations. We develop a framework that supports niches of experts in the crowd in adding annotation of high quality. This thesis focuses on search strategies that match experts with artworks to annotate. Our approach uses explicit semantics for modeling the relations between the properties of the collection items, content-based filtering aimed at diversification, and trust-aware ranking of the results. | |||
| Enforcing Privacy in Secondary User Information Sharing and Usage | | BIBAK | Full-Text | 396-400 | |
| Johnson Iyilade | |||
| Secondary user information sharing and usage for purposes other than what it
was primarily collected for has become an increasing trend, especially, as we
witness a surge in the volume of data collected from and about users online.
Although, allowing secondary sharing and usage of data in new and innovative
ways is beneficial to the user and the society at large, it also poses the
privacy risks of sharing and using personal information for unintended
purposes. This paper discusses my PhD thesis towards creating a privacy
framework for secondary user information sharing. The aim is to develop an
infrastructure that enables sharing of user information across applications and
services for beneficial purposes, while balancing it with protecting the user
against the potential privacy risks. This paper discusses current work and open
challenges. Keywords: Privacy; Personalization; User Modeling; Secondary User Data Sharing; Policy | |||
| Modeling Programming Skills of Students in an Educational Recommender System | | BIBAK | Full-Text | 401-404 | |
| Štefan Pero | |||
| We present a so-called supervised educational recommendation framework in
this paper aiming to recommend those programming tasks for a student which
improve his skills and performance. The main issue of this approach is an
appropriate student model w.r.t. his skills and other implicit factors. The
student model can be derived from the solutions provided by the student and the
teacher's (textual as well as numerical) evaluation of these solutions. Keywords: educational recommendation; student model; personalization | |||
| Socially Adaptive Electronic Partners for Socio-geographical Support | | BIBA | Full-Text | 405-408 | |
| Abdullah Kayal | |||
| Social software have been successful in gathering a large number of users in the industrialized world. An opportunity exists in utilizing social software to enhance the quality of life of ourselves and those important to us. In our research we focus on elementary school children as they begin to discover their surrounding areas, and become more involved in interaction with their peers. We explore the possibility of providing socio-geographical support by creating a system of electronic partners (or ePartners), which are intelligent agents that function as teammates to their human users. Since social contexts and familial situations can vary, it is crucial that ePartners are capable of providing personalized support. We aim to achieve that by providing a rich specification language, allowing users to enter their social requirements into the ePartner as norms. | |||
| An Adaptive Spellchecker and Predictor for People with Dyslexia | | BIBAK | Full-Text | 409-413 | |
| Alberto Quattrini Li | |||
| Spellcheckers/predictors can help people in writing more efficiently. It is
a well-known fact, for example, that spellcheckers/predictors can ease writing
for people with dyslexia. However, most of the spellcheckers assume that wrong
words contain just few errors (the literature claims that 80% to 95% of
spelling errors contain one error), in terms of the four classical edit
operation (i.e., addition, deletion, transposition, substitution), and that
errors are isolated (i.e., each error involves just one word). In addition,
since standard spellcheckers do not use context, they are not able to correct
real-word errors. Finally, they usually are not predictors. This feature is
very useful for people with dyslexia, as it allows them to type less
characters. The aim of my research is to address the aspect of adaptation and
personalization to the individual behavior for the model and the user interface
of spellchecker/predictor, considering people with dyslexia. Specifically, we
designed and trained a model that takes into account the typical errors (even
real-word errors) made by people with dyslexia and the context for
spellchecking and prediction, and the experiments to carry out for evaluating
its performance. In addition, we formalized the parameters for making the
interface adaptive, so that the user interaction with the system is light. In
the next months, we will finish the development of the adaptive user interface.
Then we will conduct experimental studies for testing the system. From a
broader perspective, we try to generalize the system to other user types. Keywords: spellchecker; predictor; adaptive system; dyslexia | |||