Data management is becoming increasingly user-centric, with users shifting from their role of pure information consumers to generators and evaluators of content of all sorts. Indeed, we are witnessing the emergence of a plethora of systems, especially on the Web, in which users contribute, access and evaluate information, collaborate and interact in complex environments, either explicitly or implicitly. Prominent examples of such systems are social networking (Facebook), microblogging (Twitter), social bookmarking (Delicious), collaborative tagging and rating (Flickr, MovieLens), crowdsourcing evaluation tasks (Mechanical Turk), or Web advertising.
As users interact with those systems, they leave footprints that can be exploited to develop useful applications. In this proposal, we are interested in two families of user-centric applications: information access and intelligent crowdsourcing. A common aspect of these applications is the need to define, collect, and maintain user profiles. User profiles are the cornerstone of successful applications and need to be continuously refined to maintain quality applications. For information access – search or recommendation - preference profiles help better personalize content provided to users as a result of a search query or as a recommendation. Indeed, when dealing with users as consumers of information, applications may have to satisfy very diverse preferences about how query result relevance is assessed. For intelligent crowdsourcing, such as data sourcing and micro-task completion, expertise profiles help better assign task to users; when looking for expert users, expertise levels may be very diverse and, at the same time, difficult to understand. In both scenarios, user preferences and user expertise cannot be known in advance; also, they can rarely be expected to be declared explicitly by users in a reliable way or to remain stable over time. Consequently, preferences and expertise need to be discovered over time via mundane interactions with users, using a principled approach. Given the growth rate of rich and diverse content and of the user base, a learning approach is unavoidable. Our project’s goal is to study of models and algorithms that rely on adaptive learning techniques to improve the effectiveness, performance, and scalability of user-centric applications.
What are we aiming to do?
The main goals of the ALICIA project are to
contribute to the development of highly adaptive learning mechanisms for non-stationary, strongly contextualized information sources and needs – key features of social media and user-centric applications – while promoting information relevance, completeness and diversity in how content or users are selected in response to users’ needs. In order to deliver on this research goal, we intend to focus on adaptive learning algorithms that have the potential to perform well under the conditions that may arise in online, highly dynamic, user centric environments, such as the family of Multi-Armed Bandits algorithms.