ACM SIGCHI General Interest Announcements (Mailing List)


Options: Use Forum View

Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Cataldo Musto <[log in to unmask]>
Reply To:
Cataldo Musto <[log in to unmask]>
Thu, 26 Mar 2015 11:55:42 +0100
text/plain (141 lines)
1st Workshop on Deep Content Analytics Techniques for Personalized and Intelligent Services (DeCAT)

co-located with UMAP 2015 ( - Dublin, Ireland, 30 June 2015

eMail: [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 uncontrolled and exponential growth of the online information gave new life to the research in the area of user modelling and personalization, since information about usersÕ preferences, sentiment and opinions can now be obtained by mining data gathered from many heterogeneous sources.

As an example, many recent work rely on the analysis of the content posted by people on social networks and micro-blogs to unveil latent information about their interests, automatically extract people personality traits, build preferences models on the ground of textual reviews, and so on. 
At the same time, the recent phenomenon of (Linked) Open Data fueled this research line by making available a huge amount of machine-readable textual data.

All these trends paved the way to the design of intelligent and personalized systems able to extract some real value from this plethora of rough textual content produced on the Web: examples of such services are online brand monitoring platforms, social recommender systems and 
smart cities-related applications, as incident detection systems or personalized city tour planners.

However, a complete exploitation of such textual streams requires a comprehension of the information conveyed by people. In turn, this requires a deep understanding of the language, which is not trivial. 

The major goal of this workshop is to stimulate the attention of the scientific community on the aforementioned topics. 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 content representation and deep content analytics can be effective to build a new generation of intelligent and personalized services based on the analysis of Social, Big and Linked Open Data.

Topics of interests include but are not limited to:

•    User Modeling
o    User Modeling based on Social and Linked Open Data;
o    User Modeling based on Semantic Content Analysis;
o    User Modeling based on Big Data Analytics;
o    User Modeling based on Emotions and Personality Traits;
o    Tracking implicit feedbacks (e.g. social activities) to infer user interests;
o    Holistic User Modeling, interoperable and decentralized profiles.

•    Deep Content Representation
o    Natural Language Processing Techniques;
o    Semantics Analysis for enhanced content representation;
o    Semantics Representation based on Open Knowledge Sources (Wikipedia, DBpedia, Freebase, etc.);
o    Semantics Representation based on Entity Linking algorithms (TagMe, DBpedia Spotlight, etc.);
o    Semantics Representation based on Linked Open Data;
o    Multilingual Content representation;
o    Geometrical Semantics Models (e.g. Distributional Models);
o    New Trends in Content Representation (e.g. Deep Learning approaches).

•    Big Data, Social Data and Linked Data Mining
o    Techniques for social user data collection, aggregation and analysis
o    Social Sensing (aggregating user-based data to obtain people-based findings)
o    Opinion Mining and Sentiment Analysis of social content;
o    Network Analysis and Community Detection.
o    Privacy, Trust , Reputation and ethical issues;
o    Scalability issues and technologies for massive social data extraction;

•    Applications
o    Recommender Systems based on Social, Big and Linked Data;
o    Recommender Systems based on Emotions and Personality;
o    Adaptation and Personalization in e-Government domain;
o    Online Monitoring based on Social Data (Social CRM, Brand Analysis, etc.)
o    Location-based and Context-aware Adaptive Applications;
o    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 12 pages);
(B) Short Research papers (max 6 pages);
(C) Posters and Demo papers (max 3 pages).

Submission site:

All submitted papers will be evaluated by at least two members of the program committee, based on originality, significance, technical soundness, and clarity of expression. Papers should be formatted according to the LNCS format (detailed formatting instructions 
can be found at:

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.

The final proceedings will be published on in the joint Poster and Demo proceedings of UMAP 2015. We will be looking at the possibility of editing a journal special issue from the workshop.

* Full paper submission: March 31, 2015 (GMT)
* Paper notification: April 24, 2015
* Camera-ready paper: May 4, 2015

Lora Aroyo - VU University Amsterdam, The Netherlands - [log in to unmask] 
Geert-Jan Houben - Delft University of Technology, The Netherlands - [log in to unmask] 
Pasquale Lops - University of Bari, Italy - [log in to unmask] 
Cataldo Musto - University of Bari, Italy - [log in to unmask] 
Giovanni Semeraro - University of Bari, Italy - [log in to unmask] 

Liliana Ardissono, University of Turin, Italy 
Martin Atzmueller, University of Kassel, Germany
Alejandro Bellogín, Universidad Autonoma de Madrid, Spain
Robin Burke, DePaul University, United States
Ivan Cantador, Univ. Autonoma de Madrid, Spain
Federica Cena, University of Turin, Italy 
Paolo Cremonesi, Politecnico di Milano, Italy
Tommaso Di Noia, Politecnico di Bari, Italy 
Peter Dolog, Aalborg University, Denmark 
Alexander Felfernig, Technische Universitat Graz, Austria
Cristina Gena, University of Turin, Italy 
Fabio Gasparetti, University Roma Tre, Italy 
Alessandro Giuliani, Università degli Studi di Cagliari
Claudia Hauff, Delft University of Technology, The Netherlands
Marius Kaminskas, Insight Center, University of Ireland, Cork 
Fedelucio Narducci, University of Bari, Italy 
Simone Paolo Ponzetto, University of Mannheim, Germany 
Francesco Ricci, Free University of Bolzano, Italy 
Shaghayegh Sahebi, University of Pittsburgh, United States 
Marko Tkalcic, Johannes Kepler University, Linz, Austria 
Christoph Trattner, Norwegian University of Science and Technology (NTNU)
Markus Zanker, University Klagenfurt, Austria

Need more information?
eMail: [log in to unmask] 

    For news of CHI books, courses & software, join CHI-RESOURCES
     mailto: [log in to unmask]

    To unsubscribe from CHI-ANNOUNCEMENTS send an email to
     mailto:[log in to unmask]

    For further details of CHI lists see