It is our great pleasure to welcome you to the 26th ACM International Conference on User modeling, Adaptation and personalization - UMAP 2018. UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users or to groups of users. UMAP is the successor of the biennial User Modeling (UM) and Adaptive Hypermedia and Adaptive Web-based Systems (AH) conferences that were merged in 2009. It has traditionally been organized under the auspices of User Modeling Inc. Since 2016, UMAP is an ACM conference, sponsored by ACM SIGCHI and SIGWEB.
UMAP 2018 is a very special conference, as this is the very first time UMAP will be located in Asia! We hope to meet many like-minded researchers from Singapore and other Asian countries. The conference spans a wide scope of topics related to user modeling, adaptation, and personalization. UMAP 2018 is focused on bringing together cutting-edge research from user interaction and modeling, adaptive technologies, and delivery platforms. It includes high-quality peer-reviewed papers featuring substantive new research in one of five research tracks, each chaired by leaders in the field:
Adaptive Hypermedia and the Semantic Web (track chairs Peter Brusilovsky and Geert-Jan Houben)
Intelligent User interfaces (track chairs Shlomo Berkovsky and Markus Schedl)
Personalized Recommender Systems (track chairs Dietmar Jannach and Markus Zanker)
Personalized Social Web (track chairs Cecile Paris and Julita Vassileva)
Technology-Enhanced Adaptive Learning (track chairs Olga Santos and Carla Limongelli)
The call for papers attracted 137 submissions from 33 different countries on all continents except Antarctica: Argentina, Australia, Austria, Belgium, Brazil, Canada, China, Cyprus, Denmark, Finland, France, Germany, India, Indonesia, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Philippines, Portugal, Saudi Arabia, Singapore, South Korea, Spain, Sweden, Switzerland, United Kingdom, and the United States The international program committee consisted of 131 reviewers. Each submission received at least 3 reviews. After the initial reviews were submitted, the designated track chairs (TCs) facilitated discussion amongst reviewers in order to resolve differences and correct misunderstandings. The TCs then provided a recommendation to the Program Chairs. The final decisions were based on these recommendations, meta-reviews, and reviewer scores.
Moreover, 10 papers were accepted as extended abstracts, and 13 were included in Late Breaking Results track (LBR). We thank Hui Fang and Pasquale Lops, LBR and Demo Chairs, for their efforts on selecting addition papers submitted to this track. As a result, there are 3 Demos, 3 Theory, Opinion and Reflection papers, and 20 Late Breaking Results papers presented in the iv UMAP poster sessions, which collectively showcase the wide spectrum of novel ideas and latest results in user modeling, adaptation and personalization.
We also encourage attendees to attend the keynote presentations; these valuable and insightful talks guide us to a better understanding of the future.
Running Recommendations: Personalisation Opportunities for Health and Fitness, Barry Smith (University College Dublin, Ireland)
Robots that Listen to People's Hearts: the Role of Emotions in the Communication between Humans and Social Robots, Ana Paiva (University of Lisbon, Portugal)
Interpreting User Input Intention in Natural Human Computer Interaction, Yuanchun Shi (Tsinghua University, China)
UMAP 2018 HUM (Holistic User Modeling) Workshop Chairs' Preface &Organization
It is our great pleasure to welcome you to the UMAP 2018 HUM (Holistic User Modeling) Workshop. According to a recent claim by IBM, 90% of the data available today have been created in the last two years. This exponential growth of online information ...
Tourist Support System Using User Context Obtained from a Personal Information Device
Tour planning is a difficult task for those who visit unfamiliar city destinations. Furthermore, building an itinerary becomes more difficult as the number of options, which can be incorporated into travel, increases. The authors aim to propose place of ...
A Framework for Holistic User Modeling Merging Heterogeneous Digital Footprints
In this paper we introduce the concept of holistic user profile, intended as a unique representation of a user that merges the heterogeneous footprints she spread on social networks and through personal devices, and we present a framework that supports ...
iSynchronizer: A Tool for Extracting, Integration and Analysis of MovieLens and IMDb Datasets
The growing popularity of e-commerce has ignited the interest of the research community in e-commerce application research and development. For this purpose, variety of applications and resources such as MovieLens and IMDb datasets have been utilized, ...
Holistic User Models for Cognitive Disabilities: Personalized Tools for Supporting People with Autism in the City
- Amon Rapp,
- Federica Cena,
- Claudio Mattutino,
- Alessia Calafiore,
- Claudio Schifanella,
- Elena Grassi,
- Guido Boella
This paper presents a personalized interactive map aimed at supporting people with Autism Spectrum Disorder (ASD) in their daily transfers within urban environments. To this end, it aims to model a "complete" representation of the ASD individual by ...
Me, Myself and I: Are Looking for a Balance between Personalization and Privacy
Websites are more than ever tailoring themselves to their customers, gathering and using the information they are providing in order to offer a differentiated product. Most people are aware of their browser's history and cookies, but with the rise of ...
Interactive Recommendations by Combining User-Item Preferences with Linked Open Data
Recent advances in graph and network embeddings have been utilized for the purpose of providing recommendations. Hybrid recommender systems have shown the efficacy of using side information associated with entities. In this work we show how domain ...
Injecting Semantic Diversity in Top-N Recommender Systems Using Determinantal Point Processes and Curated Lists
Top-N Recommender Systems usually suffer from intra-list diversity as they are tailored for relevance and predicted rating accuracy. This problem is magnified in the case of cold start setting - resulting in users being restricted to popular set of ...
Predicting Learning Difficulty Based on Gaze and Pupil Response
E-Learning is transforming the way education is imparted. Today, millions of students take self-paced online courses. However, the content and language complexity often hinders comprehension and this together with lack of immediate help from the course ...