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27th UMAP 2019: Larnaca, Cyprus
- George Angelos Papadopoulos, George Samaras, Stephan Weibelzahl, Dietmar Jannach, Olga C. Santos:

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, Larnaca, Cyprus, June 9-12, 2019. ACM 2019, ISBN 978-1-4503-6021-0
Keynote & Invited Talks
- George A. Papadopoulos, George Samaras, Stephan Weibelzahl:

Session details: Keynote & Invited Talks. - Marios N. Avraamides:

Modeling Behavior and Designing Evidence-Based Technologies: What We Can Learn for Empirical Data. 1 - Mounia Lalmas:

Engagement, Metrics and Personalisation: the Good, the Bad and the Ugly. 2 - Judith Masthoff:

Towards Utter Well-Being: Personalization for Guardian Angels. 3
ACM UMAP 2019 Main Track
- Dietmar Jannach, Olga C. Santos:

Session details: ACM UMAP 2019 Main Track. - Cataldo Musto

, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro
:
Justifying Recommendations through Aspect-based Sentiment Analysis of Users Reviews. 4-12 - Thi Ngoc Trang Tran

, Müslüm Atas, Alexander Felfernig, Viet Man Le, Ralph Samer, Martin Stettinger:
Towards Social Choice-based Explanations in Group Recommender Systems. 13-21 - Chun-Hua Tsai, Peter Brusilovsky:

Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance. 22-30 - Vladimir Guchev, Federica Cena, Fabiana Vernero, Cristina Gena:

Visual Annotations for Hybrid Graph-based User Model. 31-35 - Yelena Mejova

, Kyriaki Kalimeri:
Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions. 36-45 - Helma Torkamaan, Jürgen Ziegler:

Rating-based Preference Elicitation for Recommendation of Stress Intervention. 46-50 - Eva Geurts

, Fanny Van Geel, Peter Feys
, Karin Coninx:
WalkWithMe: Personalized Goal Setting and Coaching for Walking in People with Multiple Sclerosis. 51-60 - Andisheh Partovi, Ingrid Zukerman, Kai Zhan, Nora Hamacher, Jakob Hohwy:

Relationship between Device Performance, Trust and User Behaviour in a Care-taking Scenario. 61-69 - Alexander Prange, Daniel Sonntag:

Modeling Cognitive Status through Automatic Scoring of a Digital Version of the Clock Drawing Test. 70-77 - Bruno Massoni Sguerra

, Pierre Jouvelot:
"An Unscented Hound for Working Memory" and the Cognitive Adaptation of User Interfaces. 78-85 - Oludamilare Matthews, Sukru Eraslan, Victoria Yaneva, Alan Davies

, Yeliz Yesilada
, Markel Vigo
, Simon Harper
:
Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web: A Case Study of Neurotypical People vs People with Autism. 86-94 - Pinar Barlas

, Styliani Kleanthous
, Kyriakos Kyriakou
, Jahna Otterbacher:
What Makes an Image Tagger Fair? 95-103 - Matthias Wölbitsch, Simon Walk, Michael Goller, Denis Helic

:
Beggars Can't Be Choosers: Augmenting Sparse Data for Embedding-Based Product Recommendations in Retail Stores. 104-112 - Stav Yanovsky, Nicholas Hoernle, Omer Lev, Kobi Gal

:
One Size Does Not Fit All: Badge Behavior in Q&A Sites. 113-120 - Muesluem Atas, Ralph Samer, Alexander Felfernig, Thi Ngoc Trang Tran

, Seda Polat Erdeniz, Martin Stettinger:
Socially-Aware Diagnosis for Constraint-Based Recommendation. 121-129 - Daniel Herzog, Wolfgang Wörndl:

A User Study on Groups Interacting with Tourist Trip Recommender Systems in Public Spaces. 130-138 - Dimitris Sacharidis

:
Diversity and Novelty in Social-Based Collaborative Filtering. 139-143 - Jacek Wasilewski, Neil Hurley:

Bayesian Personalized Ranking for Novelty Enhancement. 144-148 - Cesare Bernardis, Maurizio Ferrari Dacrema

, Paolo Cremonesi:
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems. 149-156 - Shir Frumerman, Guy Shani, Bracha Shapira

, Oren Sar Shalom:
Are All Rejected Recommendations Equally Bad?: Towards Analysing Rejected Recommendations. 157-165 - Martin Lopatka, Victor Ng, Ben Paul Miroglio, David Zeber, Alessio Pierluigi Placitelli, Laura Thomson:

Telemetry-Aware Add-on Recommendation for Web Browser Customization. 166-175 - Ramtin Keramati, Emma Brunskill:

Value Driven Representation for Human-in-the-Loop Reinforcement Learning. 176-180 - Noemi Mauro

, Liliana Ardissono
:
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests. 181-190 - Daniela Stier, Ellen Sigloch:

Linguistic Design of In-Vehicle Prompts in Adaptive Dialog Systems: An Analysis of Potential Factors Involved in the Perception of Naturalness. 191-195 - Chelsea M. Myers, David Grethlein, Anushay Furqan, Santiago Ontañón, Jichen Zhu:

Modeling Behavior Patterns with an Unfamiliar Voice User Interface. 196-200 - Argyris Constantinides, Marios Belk, Christos Fidas

, Andreas Pitsillides:
On the Accuracy of Eye Gaze-driven Classifiers for Predicting Image Content Familiarity in Graphical Passwords. 201-205 - Adriano Arra, Alessio Bianchini, Joana Chavez, Pietro Ciravolo, Fatjon Nebiu, Martina Olivelli

, Gabriele Scoma, Simone Tavoletta, Matteo Zagaglia, Alessio Vecchio:
Personalized Gait-based Authentication Using UWB Wearable Devices. 206-210 - Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, Tommy Sandbank:

Detecting Persuasive Arguments based on Author-Reader Personality Traits and their Interaction. 211-215 - Noemi Mauro

, Liliana Ardissono
, Zhongli Filippo Hu
:
Multi-faceted Trust-based Collaborative Filtering. 216-224 - Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher

:
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. 225-233 - Laura Burbach, Patrick Halbach, Martina Ziefle, André Calero Valdez

:
Who Shares Fake News in Online Social Networks? 234-242 - Panagiotis Mavridis, Owen Huang, Sihang Qiu, Ujwal Gadiraju, Alessandro Bozzon

:
Chatterbox: Conversational Interfaces for Microtask Crowdsourcing. 243-251 - Vikas Ashok

, Syed Masum Billah, Yevgen Borodin, I. V. Ramakrishnan:
Auto-Suggesting Browsing Actions for Personalized Web Screen Reading. 252-260 - Pranut Jain

, Rosta Farzan, Adam J. Lee
:
Adaptive Modelling of Attentiveness to Messaging: A Hybrid Approach. 261-270 - Aadhavan M. Nambhi, Bhanu Prakash Reddy, Aarsh Prakash Agarwal, Gaurav Verma, Harvineet Singh, Iftikhar Ahamath Burhanuddin:

Stuck? No worries!: Task-aware Command Recommendation and Proactive Help for Analysts. 271-275 - Yu Liang

, Martijn C. Willemsen
:
Personalized Recommendations for Music Genre Exploration. 276-284 - Jaehun Kim, Andrew M. Demetriou

, Sandy Manolios
, Cynthia C. S. Liem
:
Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference. 285-293 - Yucheng Jin

, Nyi Nyi Htun
, Nava Tintarev, Katrien Verbert
:
ContextPlay: Evaluating User Control for Context-Aware Music Recommendation. 294-302 - Mohammad Hossein Rimaz, Mehdi Elahi, Farshad Bakhshandegan Moghaddam, Christoph Trattner, Reza Hosseini, Marko Tkalcic

:
Exploring the Power of Visual Features for the Recommendation of Movies. 303-308 - Dereck Toker, Róbert Móro

, Jakub Simko
, Mária Bieliková, Cristina Conati:
Impact of English Reading Comprehension Abilities on Processing Magazine Style Narrative Visualizations and Implications for Personalization. 309-317 - Muhammad Adamu Sidi-Ali

, Judith Masthoff, Matt Dennis
, Jacek Kopecký
, Nigel A. Beacham
:
Adapting Performance And Emotional Support Feedback To Cultural Differences. 318-326 - Nigel Bosch

, Eddie Huang, Lawrence Angrave
, Michelle Perry:
Modeling Improvement for Underrepresented Minorities in Online STEM Education. 327-335 - Fidelia A. Orji, Kiemute Oyibo, Rita Orji, Jim E. Greer, Julita Vassileva:

Personalization of Persuasive Technology in Higher Education. 336-340
Doctoral Consortium
- Laurens Rook, Markus Zanker:

Session details: Doctoral Consortium. - Muhammad Adamu Sidi-Ali

:
Adaptive E-Learning: Motivating Learners whilst Adapting Feedback to Cultural Background. 341-344 - Makuochi Nkwo

:
Designing Culturally-appropriate Persuasive Technology to Promote Positive Work Attitudes among Workers in Public Workplaces. 345-348 - Alessia Antelmi

:
Towards an Exhaustive Framework for Online Social Networks User Behaviour Modelling. 349-352 - Noa Tuval:

Exploring the Potential of the Resolving Sets Model for Introducing Serendipity to Recommender Systems. 353-356

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