*** Apologies for cross postings ***

Holistic User Modeling - Merging Quantified Self and Semantic Content
(HUM@UMAP 2017)

co-located with UMAP 2017 (http://www.um.org/umap2017/) - Bratislava
(Slovakia) -
9-12th July 2017

Twitter: https://twitter.com/HUM_Workshop
Web: https://hum17.wordpress.com/

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 gave new life to research in the area of user modelling and
personalization, since information about users’ preferences, sentiment and
opinions as well as signals describing their physical and psychological
state can now be obtained by mining data gathered from many heterogeneous
Such sources can be roughly classified into two categories: on one side,
the recent trend of Quantified Self (QS) and Personal Informatics
emphasized the use of technology to collect personal data on different
aspects of people’s daily lives. These data can be internal states (such as
mood or glucose level) or indicators of performance (such as the kilometres
run). The purpose of collecting these data is self-monitoring, performed to
gain self-knowledge or to obtain some change or improvement (behavioural,
psychological, therapeutic, etc.). Often these data are also exploited for
behaviour change purposes, for example to increase the user’s physical
At the same time, an enormous amount of textual content is continuously
spread on social networks, and this has driven a strong research effort to
investigate to what extent such data can be exploited to infer user
interests, personality traits, emotions, and knowledge. Moreover, the
recent phenomenon of (Linked) Open Data fuelled this research line by
making available a huge amount of machine-readable textual data that can be
used to connect all the data points spread in different data silos under a
uniform representation formalism.
The main research questions, which arise from both trends, are quite
fundamental: how can we separate signal from noise and extract some real
value from this plethora of data produced through devices and social
networks? How can we effectively merge such data to obtain a holistic (and
semantic) representation of all the facets describing people? How can we
use such data to trigger personalization and adaptation mechanisms? Are
there new opportunities for providing extremely tailored services for
supporting users in their everyday life?
What about the privacy issues, the need for transparency and the ethical
implications in the management of such data?
The workshop aims to provide a forum for discussing open problems,
challenges and innovative research approaches in the area, in order to
investigate whether the adoption of techniques for semantic representation
of textual and physiological data points can be effective to build a new
generation of personalized and intelligent systems based on the analysis of
Personal, Big and Linked Open Data.

Topics of interests include but are not limited to:

Personal, Linked and Social Data Mining
- Techniques for collection, aggregation and analysis of Personal, Linked
and Social Data
- Social Sensing (aggregating user-based data to obtain people-based
- Scalability issues and technologies for massive social data extraction
- Ethical issues, need for transparency, privacy management of Personal and
Social data
- Semantic Holistic User Profiling and EU General Data Protection
Regulation (GDPR)

Semantic Analysis of Personal, Linked and Social data
- Semantics Representation based on Open Knowledge Sources (Wikipedia,
DBpedia, Freebase, etc.)
- Semantics Representation based on Entity Linking algorithms (TagMe,
Babelfy, DBpedia Spotlight)
- Semantics Representation based on Geometrical Models (e.g. Distributional
Models, Deep Learning approaches)

User Modeling
- User Modeling based on Semantic Content Analysis of Social and Linked
Open Data
- User Modeling based on data coming from self-tracking devices
- User Modeling based on Emotions and Personality Traits
- Tracking implicit feedbacks (e.g. social activities) to infer user
- Lifelogging User Models

- Recommender Systems exploiting Personal and Linked Data
- Recommender Systems based on Emotions and Personality
- Recommender systems based on physiological data
- Recommender systems for behavior change
- Adaptation and Personalization in e-Government domain
- Online Monitoring based on Social Data (Social CRM, Brand Analysis, etc.)
- Intelligent and Personalized Smart Cities-related Applications (e.g.
Event Detection, Incident Detection, Personalized Planners, etc.)

We encourage the submission of original contributions, investigating the
impact of content analysis techniques on adaptive and personalized services:

(A) Full research papers (max 5 pages - ACM format);
(B) Short Research papers and Demos (max 2 pages - ACM format);

Submission site: https://easychair.org/conferences/?conf=hum2017

All submitted papers will be evaluated by at least two members of the
program committee, based on originality, significance, relevance and
technical quality. Papers should be formatted according to the ACM SIG
proceedings template: http://www.acm.org/publications/proceedings-template
Note that the references do not count towards page limits. Submissions
should be single blinded, i.e. authors names should be included in the

Submissions must be made through the EasyChair conference system prior the
specified deadline (all deadlines refer to GMT). At least one of the
authors should register and take part at the conference to make the

All accepted papers will be published by ACM as a joint volume of Extended
UMAP 2017 Proceedings and will be available via the ACM Digital Library. At
least one author of each accepted paper must register for the particular
workshop and present the paper there.

* Full paper submission: April 20, 2017
* Paper notification: May 20, 2017
* Camera-ready paper: May 28, 2017

Cataldo Musto - University of Bari, Italy
Amon Rapp - University of Torino, Italy
Federica Cena - University of Torino, Italy
Frank Hopfgartner - University of Glasgow
Judy Kay - University of Sydney, Australia.
Giovanni Semeraro - University of Bari, Italy

Liliana Ardissono, University of Turin, Italy
Alejandro BellogÌn, Universidad AutÛnoma de Madrid, Spain
Peter Dolog, Aalborg University, Denmark
Cristina Gena, University of Turin, Italy
Fabio Gasparetti, University ìRoma Treî, Italy
Bob Kummerfeld, University of Sydney Australia
Marco Polignano, University of Bari, Italy
Shaghayegh Sahebi, University of Pittsburgh, United States
Giuseppe Sansonetti, University ìRoma Treî, Italy
Christoph Trattner, Norwegian University of Science and Technology (NTNU)
Till Plumbaum, TU Berlin, Germany
Markus Zanker, University Klagenfurt, Austria

Amon Rapp Ph.D.

University of Torino - Computer Science Department
C.so Svizzera, 185 - Torino, Italy

Email: [log in to unmask]
Mobile: +39 346 2142386
Skype: amonrp

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