Augmenting User Models with Real World Experiences to Enhance Personalization and Adaptation
About The digital world, i.e. our interaction with computer systems, becomes more and more connected with the physical world, i.e. our real-world activities and experiences. This changes the way we use technologies and opens up new opportunities for personalization and adaptation. People blog, post, chat, comment, tweet about things that matter to them: what they had for dinner, what their job activities were, what they thought about a particular television broadcast, et cetera. People share content about their activities, e.g. pictures taken at a concert, videos of business meetings, reports on business trips, personal stories. This abundant digital information stream has become an important backchannel in our daily lives. We constantly create digital traces about our experiences, which can be invaluable source for personalization.
The time is ripe for developing new adaptation paradigms that exploit digital traces to extend users' personalized experience by connecting the digital, social and physical worlds. Hence, traditional adaptation mechanisms (such as feedback, help, guidance) can be extended to become more effective by taking into account not only the user's experience in the digital world (i.e. the conventional user modeling paradigm), but also relevant experience (of this user or of similar users) in the physical world. The latter approach, which is the focus of this workshop, represents an emerging research strand whereby user models are augmented with real world knowledge to enhance adaptation and personalization.
Digital traces can be attributed to more than one individual, e.g. a circle of friends, a scientific community or even a whole population can be characterized by topics they tweet about, or things they comment about. Furthermore, events, e.g. conferences, local or global disasters, political debates, can be modeled by the streams of digital traces generated around these events (e.g. pictures, comments, discussions and reactions). Technological advancements, such as data/text mining, information extraction, opinion mining, social signal processing, interactive story telling, intelligent media annotation, semantic alignment, media aggregation and retrieval, make it now possible to automate the processing of digital traces to enrich system's understanding about users' experiences in the physical world. This technological development brings new opportunities to the user modeling community, and at the same time, opens up new technological, social, and ethical challenges.
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