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From:
Cataldo Musto <[log in to unmask]>
Reply To:
Cataldo Musto <[log in to unmask]>
Date:
Thu, 21 Feb 2019 12:28:47 +0100
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*** Apologies for cross postings ***

ExHUM Workshop @UMAP 2019 - CALL FOR PAPERS
----------------------------------------------------------------------
Explainable and Holistic User Modelling - Transparent Personalization
Methods based on Heterogeneous Personal Data
(ExHUM@UMAP 2019)

co-located with UMAP 2019 ( <http://www.um.org/umap2019>
http://www.um.org/umap2019) - Larnaca, Cyprus
9-12 June 2019

Twitter:  <https://twitter.com/ExHUM_Workshop>
https://twitter.com/ExHUM_Workshop
Web:  <https://exhum19.wordpress.com/> https://exhum19.wordpress.com
Submission:  <https://easychair.org/conferences/?conf=exhum2019>
https://easychair.org/conferences/?conf=exhum2019
For any information:
<https://webmail.uniba.it/imp/dynamic.php?page=mailbox>
[log in to unmask];
<https://webmail.uniba.it/imp/dynamic.php?page=mailbox> [log in to unmask]

=========
ABSTRACT
=========

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
personal information has given 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 sources.

How can we use such data drive personalization and adaptation
mechanisms? How can we effectively merge such data to obtain a
holistic representation of all (or some of) the facets describing
people?
What kinds of usage scenarios can be envisioned? What kinds of
services can be enabled by all these personal data?

Moreover, as the importance of such technologies in our everyday lives
grows, it is also fundamental that the internal mechanisms that guide
personalization algorithms are as clear as possible.
It is not by chance that the recent General Data Protection Regulation
(GDPR) emphasized the users' right to explanation when people face
machine learning-based (or, generally speaking, artificial
intelligence-based) systems.
Unfortunately, the current research tends to go in the opposite
direction, since most of the approaches try to maximize the
effectiveness of the personalization strategy
(e.g., recommendation accuracy) at the expense of the explainability
and the transparency of the model.

Accordingly, other important questions arise: how we can deal with the
dichotomy between the need for effective adaptive systems based on
heterogeneous and personal data and the right to transparency and
interpretability?
Is it possible to design systems that merge several personal
information and also guarantee a transparent and sctruable
personalization strategy?

The workshop aims to provide a forum for discussing open problems,
challenges and innovative research approaches in the area.
Specificallt, we want to investigate
(1) how to build a new generation of personalized and intelligent
systems that exploit multiple data points (e.g., by combining mood
data and music preferences data to provide recommendations on music to
be listened)
(2) how to guarantee transparency and explainability in the user
modeling, adaptation and personalization processes.

======
TOPICS
======
Topics of interests include but are not limited to:

.        Transparent and Explainable Personalization Strategies
o        Scrutable User Models
o        Transparent User Profiling and Personal Data Extraction
o        Explainable Personalization and Adaptation Methodologies
o        Novel strategies (e.g., conversational recommender systems) for
building transparent algorithms
o        Transparent User Interfaces
o        Designing Transparent Interaction methodologies

.        Designing and Evaluating Explanation Algorithms
o        Explanation algorithms based on item description and item
properties
o        Explanation algorithms based on user-generated content (e.g.,
reviews)
o        Explanation algorithms based on collaborative information
o        Building explanation algorithms for opaque personalization
techniques (e.g., neural networks, matrix factorization)
o        Evaluating Transparency and Explainability in interaction or
personalization
o        Designing User Studies for evaluating transparency and
explainability

.        Architectures for Holistic User Modeling
o        Architectures for User Modeling merging heterogeneous data points
o        User Modeling based on Semantic Content Analysis of Social and
Linked Open Data
o        User Modeling based on data coming from wearable devices
o        User Modeling based on Emotions, physiology, and Personality Traits
o        Lifelogging User Models

.        Novel Use Cases for Exploiting Personal and Heterogeneous Data
o         Behavior change systems
o         Health management systems
o         Games and gamified applications
o         Recommender systems
o         e-Government domain
o         Online Monitoring based on Social Data (Social CRM, Brand
Analysis, etc.)
o         Intelligent and Personalized Smart Cities-related Applications
(e.g. Event Detection, Incident Detection, - Personalized Planners,
etc.)
o         Methodologies for including heterogeneous personal data in User
Models

.        Open Issues in Transparent and Explainable User Models and
Personalized Systems
o         Ethical issues (Fairness and Biases) in User Models and
Personalized Systems
o         Privacy management of Personal and Social data
o         Discussing Recent Regulations (GDPR) and future directions
o         Tracking implicit feedbacks (e.g. social activities) to infer user
interests

============
SUBMISSIONS
============
We encourage the submission of original contributions, investigating
novel methodologies to exploit heterogeneous personal data and
approach to build transparent and scrutable user models.

(A) Full research papers (max 4 pages + 1 reference - ACM format);
(B) Short Research papers and Demos (max 2 pages + 1 reference - ACM
format);

Submission site:  <https://easychair.org/conferences/?conf=exhum2019>
https://easychair.org/conferences/?conf=exhum2019

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>
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.

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 presentation.

All accepted papers will be published by ACM as a joint volume of
Extended UMAP 2019 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.


================
IMPORTANT DATES
===============
* Full paper submission: March 13, 2019
* Paper notification: March 27, 2019
* Camera-ready paper: April 3, 2019

=============
ORGANIZATION
=============
Cataldo Musto - University of Bari, Italy
Amon Rapp - University of Torino, Italy
Federica Cena - University of Torino, Italy
Frank Hopfgartner - University of Glasgow, UK
Judy Kay - University of Sydney, Australia
Aonghus Lawlor, University College Dublin, Ireland
Pasquale Lops, University of Bari, Italy
Giovanni Semeraro - University of Bari, Italy
Nava Tintarev, Delft University of Technology, The Netherlands

 


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