The importance of user modeling and personalization is taken for granted in several scenarios. According to this widespread paradigm, each user can be modeled to 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 rapid growth of social networks changed the rules for personalization, since the spread of these platforms radically changed and renewed many consolidated behavioral paradigms. Indeed, people today exploit these platforms for decision-making related tasks, to support causes, to provide their circles with recommendations or even to express opinions and discuss about the city or the place where they live. 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. At the same time, thanks to crowdsourcing, a huge amount of content-based information has been made available in open knowledge sources as Wikipedia and the Linked Open Data Cloud.
Given that most of the information stored in these modern data sylos is made available as textual content, a consequence, a complete exploitation of these rich information sources requires a big effort on the definition of models and techniques able to effectively process the content and to represent it in a machine-readable form, in order to unveil the latent semantics and trigger more effective personalization and adaptation pipelines. This is not a trivial task, since this process requires a deep comprehension of the language, which in turn typically requires a combination of techniques coming from Machine Learning and Natural Language Processing areas.
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) for personalization and adaptation task and to foster the design of a new generation of intelligent user-centered services.
Some questions that motivate this workshop:
- What is the impact of semantics in personalization and adaptation tasks?
- Can social media improve the representation of user interests?
- Can semantic analysis technique improve the representation of user interests?
- Can these data sylos (Wikipedia, DBpedia, Freebase) be useful for personalization and adaptation tasks?
- Which data sylos are more effective to model user interests and preferences?
- What content-based information is more useful to personalize and adapt the behavior of modern intelligent systems?
- 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?
- Can the analysis of content coming from social media provide some information about user personality traits?
- How do people deal with privacy issues? Are them willing to trade better personalization with a larger tracking of their activities on the Web?
- Is it possible to think about a novel generation of adaptive platforms able to completely exploit all the available information?
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.)