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Wed, 30 Jan 2019 17:24:16 +0100
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*** Apologies for cross postings ***

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

co-located with UMAP 2019 ( - Larnaca, Cyprus
9-12 June 2019

For any information: [log in to unmask]; [log in to unmask]


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  
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  
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 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,  
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  

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:

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:
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 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.

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

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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