HUM 2017 - International Workshop on Merging Quantified Self with Semantic Content Analytics

The importance of user modeling and personalization is taken for granted in several scenarios. According to this widespread paradigm, each user can be modeled through some (explicitly or implicitly gathered) information about her knowledge or about her preferences, in order to adapt the behavior of a generic intelligent system to her specific characteristics.
However, the recent spread of social network and self-tracking devices has totally changed the rules for personalization.
On one side, the spread of social network platforms radically changed and renewed many consolidated behavioral paradigms. Thanks to the heterogeneous nature of the discussions that take place on social networks, a lot of new data are continuously available and can be gathered and exploited to build richer and more complete user models, to discover latent communities, to infer information about users’ emotions and personality traits, and also to study very complex phenomena, such as those related to the psycho-social sphere, in a totally new way. Moreover, thanks to crowdsourcing, a huge amount of content-based information has been made available in open knowledge sources such as Wikipedia and the Linked Open Data Cloud.
At the same time, self-tracking devices are becoming more and more pervasive, and a plethora of data is today available by exploiting such tools. These devices model and track a lot of signals that pure content-based information which is commonly spread on social networks can’t actually handle. Trends in wearable and ubiquitous technologies point to the fact that in the near future almost all aspects of the individual’s life, as well as her body parameters and perhaps her cognitive states, could be detected and collected through automatic means.
As a consequence, it is very important to think about a new generation of user models that are able to effectively merge the information coming from both information sources.
The main goal of the workshop is to stimulate the discussion around problems, challenges and research directions regarding the exploitation of content-based information sources (Big, Social and Linked Data) along with Personal Information sources (gathered through personal devices) for personalization and adaptation task and to foster the design of a new generation of intelligent user-centered systems.
Some questions that motivate this workshop:
What is the impact of semantics in personalization and adaptation tasks?
Does a semantic representation of the information improve the effectiveness of personalization tasks?
Does a semantic representation of the information improve the transparency of such platforms?
Which data silos (Wikipedia, DBpedia, Freebase) are more effective to model user interests and preferences?
How to exploit data coming from self-tracking devices in adaptive and personalized applications?
Is it possible to use semantics to represents data coming from self-tracking devices?
How to effectively merge data coming from social network with those coming from self-tracking devices for user modeling and personalization purposes?
How do people deal with privacy issues? Are them willing to trade better personalization with a larger tracking of their activities on the Web and in everyday life?
Is it possible to think about a novel generation of adaptive platforms able to completely exploit all the available data points?
What kind of novel personalized services can be enabled by such new data sources and their integration?
Topics of interest
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 findings)
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 interests;
Lifelogging User Models
Applications
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.)