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Cataldo Musto <[log in to unmask]>
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Cataldo Musto <[log in to unmask]>
Tue, 5 Mar 2019 12:51:49 +0100
text/plain (359 lines)
*** 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





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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 scrutable

personalization strategy?


The workshop aims to provide a forum for discussing open problems,

challenges and innovative research approaches in the area.

Specifically, 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

o        Explanation algorithms based on user-generated content (e.g.,

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

o        Designing User Studies for evaluating transparency and


.        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


.        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


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 Sheffield, 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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