Keynote speakers
Modeling the Learner in 4-D
Ryan Baker
Abstract: In recent years, student modeling research has increasing moved from modeling what a student knows to modeling a broader range of dimensions of the student, including engagement, affect, and meta-cognition. In this talk, I discuss my group's recent work to model students in a more multi-dimensional fashion, including our recent work to predict whether a student has achieved robust learning that prepares them for future learning. I discuss the additive benefits of modeling learners along multiple dimensions. I will conclude with a discussion of "discovery with models" analyses that our research has produced, expanding basic understanding of human learning, and the possibilities for richer and more flexible adaptive personalization created by these developments.
Bio: Ryan S. J. d. Baker is Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute, with a collaborative appointment in Computer Science. As of September 2012, he will be the Julius and Rosa Sachs Distinguished Lecturer at Teachers College Columbia University. He graduated from Carnegie Mellon University in 2005, with a Ph.D. in Human-Computer Interaction, and was a Post-Doctoral Fellow at Carnegie Mellon and the University of Nottingham, and first Technical Director of the Pittsburgh Science of Learning Center DataShop. In the summer of 2011, he was elected founding President of the International Educational Data Mining Society. He is Associate Editor of the Journal of Educational Data Mining. His scientific articles have won four awards, and have been nominated for eight others.
Contextualizing Useful Recommendations
Francesco Ricci
Abstract: Recommender Systems (RSs) are now popular tools and techniques providing suggestions for items to be of use to a user. RSs narrow down the overwhelming amount of information and choices available today on the Web by predicting what items are more likely to be interesting to the user. They track users’ actions that signal their preferences, and aggregate them into predictive (user) models. State of the art user models still cannot fully explain and predict the needs of the user while is searching for new items. In fact, the specific ephemeral needs of the user, the context of the search, and the context of items’ usage, do influence the user’s response to and evaluation for items. Hence, RSs should take into account this information to deliver more useful (perceived) recommendations. Context modeling and context-dependent reasoning is a complex subject and there are still major technical and practical difficulties to solve: obtain sufficient and reliable data describing the user preferences in context; selecting the right context information, i.e., relevant in a particular personalization task; understanding the impact of the contextual dimensions on the user decision making process; embedding the contextual dimensions in a recommendation computational model. These topics will be illustrated in the talk, making examples taken from the recommender systems that we have developed.
Bio: Francesco Ricci is an associate professor of computer science at the Free University of Bozen-Bolzano, Italy. His current research interests include recommender systems, intelligent interfaces, mobile systems, machine learning, case-based reasoning, and the applications of ICT to tourism. He has published more than one hundred of academic papers on these topics. He is on the editorial board of Journal of Information Technology and Tourism and the Journal of User Modeling and User Adapted Interaction. He is member of the steering committee of the ACM Conference on Recommender Systems. He served on the program committees of several conferences, including as a program co-chair of the ACM Conference on Recommender Systems (RecSys), the International Conference on Case-Based Reasoning (ICCBR) and the International Conference on Information and Communication Technologies in Tourism (ENTER).
Program
(version 2012.07.11)
Overiew:
Workshops
| Monday (2012/7/16) @ UQAM |
| Time/Venue | SH3120 @UQAM | SH3140 @UQAM | SH3220 @UQAM | SH3420 @UQAM | SH3620 @UQAM | |
| 8:00–5:00 | Registration (Foyer SH) |
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| 9:00–12:30 |
TVM2P: TV and Multimedia Personalization (site)(proceedings) |
Tutorial 1: NewGUMS: Designing and evaluating new generation user models (site) | ||||
| 1:30–5:00 |
TRUM: Trust, Reputation and User Modeling (site)(proceedings) |
SASWeb: Semantic and Adaptive Social Web (site)(proceedings) |
Tutorial 2: Empirical Evaluation of User Modeling Systems (site) |
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| Time/Venue | SH 3120 3420 @UQAM | SH3140 @UQAM | SH3220 @UQAM | SH 3420 3120 @UQAM | SH3620 @UQAM | |
| 8:00–5:00 | Registration (Foyer SH) |
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| 9:00–12:30 | SRS: Social Recommender Systems (full day) (site) (proceedings) (schedule@cn3, jul 17) | PALE: Personalization Approaches in Learning Environments (full day) (site)(proceedings) (schedule@cn3, jul 17) | PATCH: Personal Access to Cultural Heritage (site)(proceedings) (schedule@cn3, jul 17) |
FactMod: Matrix Factorization techniques for student skills and user preference modeling (site)(proceedings) (schedule@cn3, jul 17) |
Tutorial 3: Evaluation of Adaptive Systems (full day) (site)(schedule@cn3, jul 17) | |
| 1:30–5:00 | AUM: Augmented (proceedings) (schedule@cn3, jul 17) |
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Main Conference
| Wednesday (2012/7/18) @ Holiday Inn Midtown |
| Time/Venue | Ambassadeur C | Les Verrières |
| 8:00–5:00 | Registration (Foyer Bonnet) |
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| 8:45–9:00 | Welcome | |
| 9:00–10:30 | Keynote
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| 10:30‐11:00 | Coffee Break |
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| 11:00–12:00 |
Paper session 1: User Engagement
Chair: Lora Aroyo
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Paper session 2: Trust
Chair: Peter Brusilovsky
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| 12:00 ‐ 2:00 | Lunch Time |
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| 2:00– 3:30 |
Paper session 3: Motivation, Effort, and Attention
Chair: Julita Vasileva
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Paper session 4: Recommender Systems: Factorization and domain ranking
Chair: Alan Said
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| 3:30– 4:00 | Coffee Break |
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| 4:00– 5:15 |
Paper session 5: Modeling users' activities and interests
Chair: Pearl Pu
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Paper session 6: Modeling learners
Chair: Gordon McCalla
Exploring Gaze Data for Determining User Learning with an Interactive Simulation (summary) (full text)
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| 5:15– 5:45 | Poster Madness |
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| 6:30– 8:30 |
RECEPTION, POSTERS & DEMONSTRATIONS (@ UQAM – CO-500)
Conference Navigator 3: An Online Social Conference Support System (summary@cn3)
(full text)
Evaluating Learning Factors Analysis (summary@cn3)
(full text)
An Interoperable and Inclusive User Modelling concept for Simulation and Adaptation (summary@cn3)
(full text)
Developing a Scale for Assessing Instructor Attitudes Towards Open Learner Models (summary@cn3)
(full text)
Holistic accessibility evaluation using VR simulation of users with special needs (summary@cn3)
(full text)
Next-Generation Social TV Content Discovery (summary@cn3)
(full text)
Adaptive Information Visualization - Predicting user characteristics and task context from eye gaze (summary@cn3)
(full text)
GALE: Generic Adaptation Language and Engine (summary@cn3)
(full text)
Multilingual User Modeling for Personalized Re-ranking of Multilingual Web Search Results (summary@cn3)
(full text)
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| Thursday (2012/7/19) @ Holiday Inn Midtown |
| Time/Venue | Ambassadeur C | Les Verrières |
| 8:30–9:00 | Registration (Foyer Bonnet) |
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| 9:00–10:30 |
Panel: A UMUAI view of UMAP, Advances Made and Challenges Ahead
Chair: Alfred Kobsa.
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| 10:30–11:00 | Coffee Break |
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| 11:00–11:45 |
Paper session 7: User models from microblogging (plenary)
Chair: Paul de Bra
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| 11:45 – 1:30 | Lunch Time |
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| 1:30– 3:30 |
Doctoral Consortium session 1
Chair: Robin Cohen
User Feedback and Preferences Mining (summary@cn3)
Resolving Data Sparsity and Cold Start in Recommender Systems (summary@cn3)
Towards a generic model for user assistance (summary@cn3)
Multi-source provenance-aware user interest profiling on the Social Semantic Web (summary@cn3)
Formalising Human Mental Workload as Non-monotonic concept for Adaptive and Personalised Web-design (summary@cn3)
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Doctoral Consortium session 2
Chair: Lora Aroyo
Evaluating an Implementation of an Adaptive Game-based Learning Architecture (summary@cn3)
A Data Mining Approach for Adding Adaptive Interventions to Exploratory and Open-Ended Environments summary@cn3)
Improving matrix factorization techniques of student test data with partial order constraints (summary@cn3)
Detecting, acquiring and exploiting contextual information in personalized services (summary@cn3)
Ubiquitous Fuzzy User Modeling for Multi-Application Environments by Mining Socially Enhanced Online Traces (summary@cn3)
Facilitating code examples search on the Web through expertise personalization (summary@cn3)
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| 3:30– 4:00 | Coffee Break |
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| 4:00– 5:00 |
Industry papers (plenary)
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| 5:30 | Departure to the Banquet | |
| 6:30 | Pre‐dinner cocktail | |
| 7:30 | BANQUET (Best paper awards announcement during the banquet ) |
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| Friday (2012/7/20) @ Holiday Inn Midtown |
| Time/Venue | Ambassadeur C | Les Verrières |
| 8:30–9:00 | Registration (Foyer Bonnet) |
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| 9:00–10:30 | Keynote
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| 10:30–11:00 | Coffee Break |
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| 11:00–12:00 |
Paper session 8: Visualizations
Chair: Alexandros Paramythis
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Paper session 9: Educational Data Mining
Chair: Kalina Yacef
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| 11:45 – 2:00 | Lunch Time |
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| 2:00– 3:30 |
Paper session 10: Recommender Systems: Critiquing, Spamming and Noise
Chair: Eelco Herde
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Paper session 11: User Centred Design and Evaluation
Chair: Stephan Weibelzahl
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| 3:30– 4:00 | Coffee Break |
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| 4:00–4:30 | Closing Ceremony |
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Registration (2)
Registration to the conference must be made through credit card by following this link: gdac.uqam.ca/umap.
Workshops and posters: note that authors of workshops and poster will be offered early registration rates due to timing of the notices. Registration can be made on site for these authors and early rates will be applied.
Note that for on site payments, only credit cards (VISA or MasterCard) or cash money in Canadian dollars will be accepted.
Special Hotel rates apply until June 15.
Queries regarding the registration should be addressed to This e-mail address is being protected from spambots. You need JavaScript enabled to view it. .
Registration
Registration to the conference must be made through credit card by following this link: gdac.uqam.ca/umap. Early registration ends on June 12.
Note that for on site payments, only credit cards (VISA or MasterCard) or cash money in Canadian dollars will be accepted.
Special Hotel rates apply until June 15.
Queries regarding the registration should be addressed to This e-mail address is being protected from spambots. You need JavaScript enabled to view it. .
Accepted papers
The table below contains the (unordered) list of accepted long, short, and industry papers.
| Title | Authors | Category | Keywords |
| Preference Relation Based Matrix Factorization for Recommender Systems | Maunendra Sankar Desarkar, Roopam Saxena and Sudeshna Sarkar | Research Long Paper | Recommender systems, Matrix Factorization, Preference Relations |
| Abstract: Users in recommender systems often express their opinions about different items by rating the items on a fixed rating scale. The rating information provided by the users is used by the recommender systems to generate personalized recommendations for them. Few recent research work on rating based recommender systems advocate the use of preference relations instead of absolute ratings in order to produce better recommendations. Use of preference relations for neighborhood based collaborative recommendation has been looked upon in recent literature. On the other hand, Matrix Factorization algorithms have been shown to perform well for recommender systems, specially when the data is sparse. In this work, we propose a matrix factorization based collaborative recommendation algorithm that considers preference relations. Experimental comparisons show that the proposed method is able to achieve better recommendation accuracy over the compared baseline methods. | |||
| Personalized Network Updates: Increasing Social Interactions and Contributions in Social Networks | Shlomo Berkovsky, Jill Freyne and Gregory Smith | Research Long Paper | personalization, social network, evaluation, news feed |
| Abstract: Social networking systems originally emerged as tools for keeping up with the daily lives for friends and strangers. They have since established themselves as valuable resources and means to satisfy information needs. The challenge with information seeking through social networks is that their immense success and popularity is also a weakness, as the data deluge facing users has surpassed comfortably managed levels by individuals and can impact on the quality and relevance of the information consumed. We developed a personalized model for predicting the relevance of news feed to facilitate personalized news feeds. Results of a live analysis show that out approach successfully identifies and promotes relevant feed items with the knock on effects of increasing interactions between users and the contribution of user generated content. | |||
| A Simple but Effective Method to Incorporate Trusted Neighbors in Recommender Systems | Guibing Guo, Jie Zhang and Daniel Thalmann | Research Long Paper | Trust-aware Recommender Systems Collaborative Filtering User Preferences |
| Abstract: Providing high quality recommendations is important for online systems to assist users who face a vast number of choices in making effective selection decisions. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. To address these issues, in this paper, we propose a simple but effective method, namely "Merge", to incorporate social trust information (i.e. trusted neighbors explicitly specified by users) in providing recommendations. More specifically, ratings of a user's trusted neighbors are merged to represent the preference of the user and to find similar other users for generating recommendations. Experimental results based on three real data sets demonstrate that our method is more effective than other approaches, both in accuracy and coverage of recommendations. | |||
| Improving Construct Validity Yields Better Models of Systematic Inquiry, Even with Less Information | Michael Sao Pedro, Ryan S.J.D. Baker and Janice Gobert | Research Long Paper | science microworlds, science inquiry, inquiry assessment, behavior detector, educational data mining, construct validity, feature selection |
| Abstract: Data-mined models often achieve good predictive power, but sometimes at the cost of interpretability. We investigate here if selecting features to increase a model's construct validity and interpretability also can improve the model's ability to predict the desired constructs. We do this by taking existing models and reducing the feature set to increase construct validity. We then compare the existing and new models on their predictive capabilities within a held-out test set in two ways. First, we analyze the models' overall predictive performance. Second, we determine how much student interaction data is necessary to make accurate predictions. We find that these reduced models with higher construct validity not only achieve better agreement overall, but also achieve better prediction with less data. This work is conducted in the context of developing models to assess students' inquiry skill at designing controlled experiments and testing stated hypotheses within a science inquiry microworld. | |||
| WTF? Detecting Students who are Conducting Inquiry Without Thinking Fastidiously | Michael Wixon, Ryan S.J.D. Baker, Janice Gobert, Jaclyn Ocumpaugh and Matthew Bachmann | Research Long Paper | student modeling, educational data mining, intelligent tutoring system, science inquiry off-task behavior |
| Abstract: In recent years, there has been increased interest and research on identifying the various ways that students can deviate from expected or desired patterns while using educational software. This includes research on gaming the system, player transformation, haphazard inquiry, and failure to use key features of the learning system. Detection of these sorts of behaviors has helped researchers to better understand these behaviors, thus allowing software designers to develop interventions that can remediate them and/or reduce their negative impacts on user outcomes. In this paper, we present a first detector of what we term WTF ("Without Thinking Fastidiously") behavior, based on data from the Phase Change microworld in the Science ASSISTments environment. In WTF behavior, the student is interacting with the software, but their actions appear to have no relationship to the intended learning task. We discuss the detector development process, validate the detectors with human labels of the behavior, and discuss implications for understanding how and why students conduct inquiry without thinking fastidiously while learning in science inquiry microworlds. | |||
| Improving the Performance of Unit Critiquing | Monika Mandl and Alexander Felfernig | Research Long Paper | Recommender systems Conversational Recommendation Critiquing Systems |
| Abstract: Conversational recommender systems allow users to learn and adapt their preferences according to concrete examples. Critiquing systems support such a conversational interaction style. Especially unit critiques offer a low cost feedback strategy for users in terms of the needed cognitive effort. In this paper we present an extension of the experience-based unit critiquing algorithm. The development of our new approach, which we call nearest neighbor compatibility critiquing, was aimed at increasing the efficiency of unit critiquing. We combine our new approach with existing critiquing strategies to ensemble-based variations and present the results of an empirical study that aimed at comparing the recommendation efficiency (in terms of the number of critiquing cycles) of ensemble-based solutions with individual critiquing algorithms. | |||
| GECKOmmender: Personalised Theme and Tour Recommendations for Museums | Fabian Bohnert, Ingrid Zukerman and Junaidy Laures | Research Long Paper | Recommender systems, Cultural heritage, Personalisation, Adaptable interfaces Field study |
| Abstract: We present GECKOmmender, a mobile system for personalised theme and tour recommendations in museums, based on a digital site-map representation. Star ratings provided by visitors for seen exhibits are used to predict ratings for unvisited exhibits. The predicted ratings in turn form the basis of recommendations. These recommendations are presented in one of three display modes: StarMap -- stars on the site map, HeatMap -- colours from green to red that indicate the interestingness of exhibits (from interesting to not interesting respectively), and TourPlan -- directed personalised tours through the museum. GECKOmmender was evaluated in a field study at Melbourne Museum (Melbourne, Australia). Our results show that (1) most participants enjoyed GECKOmmender, (2) GECKOmmender's recommendations often reflected the participants' personal interests, and (3) HeatMap was the most popular display mode. | |||
| Realistic Simulation of Museum Visitors' Movements as a Tool for Assessing Sensor-based User Models | Fabian Bohnert, Ingrid Zukerman and David Albrecht | Research Long Paper | Cultural heritage Performance tests Sensor-based systems Simulation of behaviour |
| Abstract: We present a realistic simulation framework to examine the impact of sensor noise on the performance of user models in the museum domain. Our contributions are (1) models to simulate noisy visit trajectories as time-stamped sequences of (x,y) positional coordinates which reflect walking and hovering behaviour; (2) a discriminative inference model that distinguishes between hovering and walking on the basis of (simulated) noisy sensor observations; (3) a model that infers viewed exhibits from hovering coordinates; and (4) a model that predicts the next exhibit on the basis of inferred (rather than known) viewed exhibits. Our staged evaluation assesses the effect of these models (in combination with sensor noise) on inferential and predictive performance, thus shedding light on the reliability attributed to inferences drawn from sensor observations. | |||
| Users and Noise: Estimating the Magic Barrier of Recommendation Systems | Alan Said, Brijnesh Jain, Sascha Narr and Till Plumbaum | Research Long Paper | Recommender Systems, evaluation, error measures user-generated noise |
| Abstract: Recommender systems are crucial components of most commercial websites to keep users satisfied and to increase revenue. Thus, a lot of effort is made to improve recommendation accuracy. But when is the best possible performance of the recommender reached? The magic barrier, refers to some unknown level of prediction accuracy a recommender system can attain. The magic barrier reveals whether there is still room for improving prediction accuracy or indicates that further improvement is meaningless. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies - noise. In a case study with a commercial movie recommender, we investigate the inconsistencies of the user ratings and estimate the magic barrier in order to assess the actual quality of the recommender system. | |||
| Domain Ranking For Cross Domain Collaborative Filtering | Amit Tiroshi and Tsvi Kuflik | Research Short Paper | cross-domain recommendation, cold-start problem, collaborative-filtering |
| Abstract: In recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing recommendations on items from different domains, for example recommending movies based on the target user's book item ratings. This technique is called cross-domain recommendation. When basing recom-mendations on a source domain that is different from the target domain a question arises, from which domain should items be chosen? Is there a source domain that is a better predictor for each target domain? Do books better predict a users' taste in movies or perhaps it's their music preferences? In this study we present initial results of work in progress that ranks and maps between pairs of domains based on the ability to create recommendations in domain one using ratings of items from the other domain. The recommendations are made using cross domain collaborative filtering, and evaluated on the social networking profiles of 2148 users. Initial results show that information that is freely available in social networks can be used for cross domain recommendation and that there are differences between the source domains with respect to the quality of the recommendations. | |||
| Property-based interest propagation in ontology-based user model | Federica Cena, Silvia Likavec and Francesco Osborne | Research Long Paper | user interests propagation, ontology-based user model, similarity relatedness object properties |
| Abstract: We present an approach for propagation of user interests in ontology-based user models taking into account the properties declared for the concepts in the ontology. Starting from initial user feedback on an object, we calculate user interest in this particular object and its properties and further propagate user interest to other objects in the ontology, similar or related to the initial object. The similarity and relatedness of objects depends on the number of properties they have in common and their corresponding values. The approach we propose can support finer recommendation modalities, considering the user interest in the objects, as well as in singular properties of objects in the recommendation process. We tested our approach for interest propagation with a real adaptive application and obtained an improvement with respect to IS-A-propagation of interest values. | |||
| A framework for modeling trustworthiness of users in mobile vehicular ad-hoc networks and its validation through simulated traffic flow | John Finnson, Jie Zhang, Thomas Tran, Umar Farooq Minhas and Robin Cohen | Research Long Paper | modeling user trust, transportation applications, updating user models in dynamic environments |
| Abstract: In this paper, we present an approach for modeling user trustworthiness when traffic information is exchanged between vehicles in transportation environments. Our multi-faceted approach to trust modeling combines priority-based, role-based and experience-based trust, integrated with a majority consensus model influenced by time and location, for effective route planning. The proposed representation for the user model is outlined in detail (integrating ontological and propositional elements) and the algorithm for updating trust values is presented as well. This trust modeling framework is validated in detail through an extensive simulation testbed that models vehicle route planning. We are able to show decreased average path time for vehicles when all facets of our trust model are employed in unison. Included is an interesting confirmation of the value of distinguishing direct and indirect observations of users. | |||
| Towards Adaptive Information Visualization: On the Influence of User Characteristics | Dereck Toker, Cristina Conati, Giuseppe Carenini and Mona Haraty | Research Long Paper | User characteristics, User Evaluation, Adaptive Information Visualization |
| Abstract: The long-term goal of our research is to design information visualization systems that adapt to the specific needs, characteristics, and context of each individual viewer. In order to successfully perform such adaptation, it is crucial to first identify characteristics that influence an individual user's effectiveness, efficiency, and satisfaction with a particular information visualization type. In this paper, we present a study that focuses on investigating the impact of four user characteristics (perceptual speed, verbal working memory, visual working memory, and user expertise) on the effectiveness of two common data visualization techniques: bar graphs and radar graphs. Our results show that certain user characteristics do in fact have a significant effect on task efficiency, user preference, and ease of use. We conclude with a discussion of how our findings could be effectively used for an adaptive visualization system. | |||
| Studies to Determine User Requirements Regarding In-Home Monitoring Systems | Melanie Larizza, Ingrid Zukerman, Fabian Bohnert, Andy Russell, Lucy Busija, David Albrecht and Gwyn Rees | Research Long Paper | Healthcare, Elderly needs, Smart environments, Mobile systems, Requirements gathering |
| Abstract: The ageing of the world population has led to an increased number of older people staying at home, requiring different levels of care. MIA is a user-centric project aimed at monitoring and assisting elderly people remain safely in their homes, where the design of the system is informed by the requirements of the stakeholders. In this paper, we present the results of two user studies aimed at ascertaining the expectations, concerns and priorities of elderly people and their informal carers regarding in-home monitoring technologies: (1) concept mapping combined with brainstorming sessions, and (2) questionnaires. We then discuss how these requirements affect the design of in-home monitoring systems. | |||
| User Modelling Ecosystems: A User-centred Approach | Rainer Wasinger, Michael Fry, Judy Kay and Bob Kummerfeld | Research Short Paper | User modelling ecosystems, client-side and cloud-based user models, user-centered design and framework requirements |
| Abstract: The recent exponential growth in mobile applications and the growing reliance on and awareness of `user models' by end-users have led to the need to rethink the functional and end-user requirements of existing user modelling systems. This paper has two goals. Firstly, leveraging a functioning user modelling ecosystem that provides anywhere and anytime access to desktop-, web-, and mobile- applications alike, this paper identifies a current opportunity (and need) for enhancing user interaction with existing user modelling frameworks, by extending the stereotypical cloud-based user modelling approach with that of a client-based service and an accompanying synchronisation module. Secondly, and based on a study with end-users, this paper outlines the importance of a user-centred design for user modelling frameworks and reports on the functionality that end-users (rather than developers) need and want from a user modelling ecosystem. | |||
| Exploring Gaze Data for Determining User Learning with an Interactive Simulation | Samad Kardan and Cristina Conati | Research Long Paper | Eye-tacking, Eye Gaze Data, Interactive Simulations, User Classification, User Modeling |
| Abstract: This paper explores the value of eye-tracking data to assess user learning with interactive simulations (IS). Our long-term goal is to use this data in user models that can generate adaptive support for students who do not learn well with these types of unstructured learning environments. We collected gaze data from users interacting with the CSP applet, an IS for constraint satisfaction problems. Two classifiers built upon this data achieved good accuracy in discriminating between students who learn well from the CSP applet and students who do not, providing evidence that gaze data can be a valuable source of information for building user modes for IS. | |||
| Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides | Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Giovanni Semeraro, Marco de Gemmis, Mauro Barbieri, Jan Korst, Verus Pronk and Ramon Clout | Industry Long Paper | Personalized Electronic Program Guides, Explicit Semantic Analysis, Vector Space Model, Random Indexing, Logistic Regression |
| Abstract: Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this context, we assume that user preferences can be specified by program genres (documentary, sports, ...) and that an asset can be labeled by one or more program genres, thus allowing an initial and coarse preselection of potentially interesting assets. As these assets may come from various sources, program genre labels may not be consistent among these sources, or not even be given at all, while we assume that each asset has a possibly short textual description. In this paper, we tackle this problem by considering whether those textual descriptions can be effectively used to automatically retrieve the most related TV shows for a specific program genre. More specifically, we compare a statistical approach called logistic regression with an enhanced version of the commonly used vector space model, called random indexing, where the latter is extended by means of a negation operator based on quantum logic. We also apply a new feature generation technique based on explicit semantic analysis for enriching the textual description associated to a TV show with additional features extracted from Wikipedia. | |||
| A Multi-Faceted User Model for Twitter | John Hannon, Kevin McCarthy, Michael O'Mahony and Barry Smyth | Research Short Paper | Twitter, User Modeling, Tags, Analysis |
| Abstract: In this paper we describe an initial attempt to build multi-faceted user models from raw Twitter data. The key contribution is to describe a technique for categorising users and their social ties according to a collection of curated topical categories and in this way resolve much of the preference noise that is inherent within user conversations. We go on to analyse and evaluate this approach on a data set of over 240,000 Twitter users and discuss the applications of these novel user models. | |||
| A Comparative Study of Users' Microblogging Behavior on Sina Weibo and Twitter | Qi Gao, Fabian Abel, Geert-Jan Houben and Yong Yu | Research Long Paper | user modeling, user behavior, micro-blogging, comparative usage analysis, Twitter, Sina Weibo |
| Abstract: In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40~million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, we (v) investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time. | |||
| Evaluating rating scales personality | Tsvi Kuflik, Alan Wecker, Federica Cena and Cristina Gena | Research Short Paper | rating scales user study recommender systems |
| Abstract: User ratings are a valuable source of information for recommender systems: often, personalized suggestions are generated by predicting the user's preference for an item, based on ratings the users explicitly provided for other items. In past experiments we carried out in the gastronomy domain, results showed that rating scales have their own ``personality" exerting an influence on user ratings. In this paper, we aim at deepening our knowledge of the effect of rating scale personality on user ratings by taking into account new empirical settings and a different domain (a museum), and partially different rating scales. We have compared the results of these new experiments with the previous ones. Our aim is to further validate in a different application context and domain, and with different rating scales, the fact that rating scales have their own personality which affects users' rating behavior. | |||
| Adapting performance feedback to a learner's conscientiousness | Matt Dennis, Judith Masthoff and Chris Mellish | Research Short Paper | Adaptation, Motivation, Personality, Feedback, E-learning |
| Abstract: To keep a learner motivated, an intelligent tutoring system may need to adapt its feedback to the learner's characteristics. We are particularly interested in adaptation of performance feedback to the learner's personality. Following on from an earlier study that investigated the effect of generalized self-efficacy, this study examines how feedback may need to be adapted to the trait Conscientiousness from the Five Factor Model. We used a User-as-Wizard approach, with participants taking the role of the adaptive feedback generator. Participants were presented with a fictional student with a validated polarized level of Conscientiousness, along with a set of marks the student had achieved in a test. They provided feedback to the learner in the form of a short statement. We examined the level to which participants bent the truth as adaptation to the learner's conscientiousness. The study suggests that adaptation to conscientiousness may be needed: using a positive slant for highly conscientious students with failing grades. | |||
| Automating the Modeling of Learners' Erroneous Behaviors in Model-Tracing Tutors | Luc Paquette, Jean-Francois Lebeau and André Mayers | Research Short Paper | erroneous behaviors, learner modeling, model-tracing tutors |
| Abstract: Modeling the learners is an important aspect of intelligent tutoring systems. It allows tutors to provide personalized feedbacks and to assess the learners' mastery of a task. One aspect often overlooked is the modeling of erroneous behaviors that can be used to provide error specific feedbacks. This is especially true for model-tracing tutors that require the addition of erroneous procedural knowledge for each possible error. This modeling can be automated by defining a model formally describing the learners' erroneous behaviors. Our model is inspired by the Sierra theory of procedural error and is developed using ASTUS's knowledge representation. In this paper we describe how we used Sierra in order to elaborate our model of erroneous behaviors in ASTUS. | |||
| EEG estimates of engagement and cognitive workload predict math problem solving outcomes | Federico Cirett Galán and Carole R. Beal | Research Long Paper | Machine Learning, Electroencephalography, Intelligent Tutoring Systems, physiology, behavior |
| Abstract: The study goal was to evaluate whether EEG estimates of attention and cognitive workload captured as students solved math problems could be used to predict success or failure at solving the problems. Students (N = 16) solved a series of SAT math problems while wearing an EEG headset that generated estimates of sustained attention and cognitive workload each second. Students also reported on their level of frustration and the perceived difficulty of each problem. Results from SVM training indicated that problem outcomes could be correctly predicted from the combination of attention and workload signals at rates better than chance. EEG data were also correlated with students' self-report of problem difficulty. Findings suggest that relatively non-intrusive EEG technologies could be used to improve the efficacy of tutoring systems. | |||
| Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy | Yue Gong, Joseph Beck and Carolina Ruiz | Research Long Paper | student modeling, predictive accuracy, multiple classifiers, multiple distributions of student performances, performance factors analysis |
| Abstract: In this paper, we propose a generic approach to improve a student model's predictive accuracy. The approach was designed based on the assumption that student performances are sampled from multiple, other than only one, distributions and thus should be modeled by multiple classification models. We applied k-means to identify student performances sampled from those multiple distributions, using no additional features beyond binary correctness data of student responses. We trained a separate classification model for each distribu-tion and applied the learned models to unseen students to evaluate our approach. The results showed that compared to the base classifier, our proposed approach is able to improve predictive accuracy: 4.3% absolute improvement in R2 and 0.03 absolute improvement in AUC, which is not a trivial improvement considering the current state of the art in student modeling. | |||
| Models of user engagement | Janette Lehmann, Mounia Lalmas, Elad Yom-Tov and Georges Dupret | Research Long Paper | User engagement, User characteristics, User models |
| Abstract: Our research goal is to provide a better understanding of user engagement and how to measure it. Our view is that we should not speak of one main approach to measure engagement -- e.g. through one or a fixed set of metrics -- because engagement depends on the online services at hand. Instead, we should be talking of models of user engagement. By analysing a number of services offered by a large internet company, we show that it is possible to derive effectively simple models of user engagement. This paper provides a first taxonomy of user engagement patterns, allowing for a better understanding of the important characteristics of user engagement of a service or group of services. | |||
| Investigating Explanations to Justify Choice | Ingrid Nunes, Simon Miles, Michael Luck and Carlos Lucena | Research Long Paper | User Explanation, Guidelines, Patterns, Recommender Systems, Decision Support Systems |
| Abstract: Many different forms of explanation have been proposed for justifying decisions made by automated systems. However, there is no consensus on what constitutes a good explanation, or what information these explanations should include. In this paper, we present the results of a study into how people justify their decisions. Analysis of our results allowed us to extract the forms of explanation adopted by users to justify choices, and the situations in which these forms are used. The analysis led to the development of guidelines and patterns for explanations to be generated by automated decision systems. This paper presents the study, its results, and the guidelines and patterns we derived. | |||
| Attention and Selection in Online Choice Tasks | Vidhya Navalpakkam, Ravi Kumar, Lihong Li and D Sivakumar | Research Long Paper | user attention, selection, eye-tracking, precognitive factors, cognitive factors |
| Abstract: The task of selecting one among several items in a visual display is extremely common in daily life and is executed billions of times every day on the Web. Attention is vital for selection, but the end-to-end process of what draws and sustains attention, and how that influences selection, remains poorly understood. We study this in a complex multi-item selection setting, where participants selected one among eight news articles presented in a grid layout on a screen. By varying the position, saliency, and topic of the news items, we identify the relative importance of these precognitive visual and cognitive factors in attention and selection. We present a simple Markov model of attention that predicts many key features such as shifts of attention and dwell time per item. Potential applications include optimizing visual displays to effectively drive user attention. | |||
| The effect of suspicious profiles on people recommenders | Luiz Augusto Pizzato, Joshua Akehurst, Cameron Silvestrini, Irena Koprinska, Kalina Yacef and Judy Kay | Research Long Paper | people recommender systems, online dating, suspicious profiles, behavioural analysis |
| Abstract: As the world moves towards the social web, criminals also adapt their activities to these environments. Online dating websites, and more generally people recommenders, are a particular target for romance scams. Criminals create fake profiles to attract users who believe they are entering a relationship. Scammers can cause extreme harm to people and to the reputation of the website. This makes it important to ensure that recommender strategies do not favour fraudulent profiles over those of legitimate users. There is therefore a clear need to gain understanding of the sensitivity of recommender algorithms to scammers. We investigate this by (1) establishing a corpus of suspicious profiles and (2) assessing the effect of these profiles on the major classes of reciprocal recommender approaches: collaborative and content-based. Our findings indicate that collaborative strategies are strongly influenced by the suspicious profiles, while a pure content-based technique is not influenced by these users. | |||
| Inferring Personality of Online Gamers by Fusing Multiple-View Predictions | Jianqiang Shen, Oliver Brdiczka and Bo Begole | Industry Long Paper | personality, behavior analysis, social networking, sentimental analysis, virtual worlds |
| Abstract: Reliable personality prediction can have direct impact on many adaptive systems, such as targeted advertising, interface personalization and content customization. We propose an algorithm to infer a person's personality profile by fusing analytical predictions from multiple sources, including behavioral information, text analysis information and social networking information. Each individual source provides a partial view about a person's personality and by fusing the predictions from each source, we get more reliable results. We applied and validated our approach on a real data set from 3050 World of Warcraft playing characters. Besides behavioral and social networking information, we found that text analysis of names contains the strongest personality cues. | |||
| Improving tensor based recommenders with clustering | Martin Leginus, Peter Dolog and Valdas Zemaitis | Research Long Paper | tensor factorization, HOSVD, clustering |
| Abstract: Social tagging systems (STS) model three types of entities (i.e. tag-user-item) and relationships between them are encoded into a 3-order tensor. Latent relationships and patterns can be discovered by applying tensor factorization techniques like Higher Order Singular Value Decomposition (HOSVD), Canonical Decomposition etc. STS accumu- late large amount of sparse data that restricts factorization techniques to detect latent relations and also signicantly slows down the process of a factorization. We propose to reduce tag space by exploiting clus- tering techniques so that the quality of the recommendations and exe- cution time are improved and memory requirements are decreased. The clustering is motivated by the fact that many tags in a tag space are semantically similar thus the tags can be grouped. Finally, promising experimental results are presented. | |||
| Using Touch as a Predictor of Effort: What the iPad can tell us about User Affective State | David H. Shanabrook, Ivon Arroyo and Beverly Park Woolf | Research Short Paper | touch, data mining, statistical analysis, intelligent tutoring systems, human computer interaction |
| Abstract: Touch is a new and significantly different method of interacting with a computer and being adapted at a rapidly increasing rate with the introduction of the tablet computer. We log the characteristics of a students touch interaction while solving math problems on a tablet. By correlating this data to high and low effort problem solving conditions we demonstrate the ability to predict student effort level. The technique is context free, thus can potentially be applied to any computer tablet application. | |||
| Adaptive Score Reports | Diego Zapata-Rivera | Industry Short Paper | Adaptive score reports, Open student models, Educational assessment, Adaptive learning environments |
| Abstract: This paper introduces the idea of adaptive score reports that can be used to provide educational stakeholders with a personalized experience aimed at facilitating student understanding and use of assessment information. These reports can also provide additional learning opportunities for users based on assessment results. An interactive score report for students is used to illustrate opportunities for adaptation. | |||
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Invited speakers
We are thrilled to announce our two invited speakers:
Ryan Baker from Worchester Polytechnic Institute (US)
Francesco Ricci from Free University of Bozen-Bolzano
Read more: Keynote addresses
