Modeling Interactive Information Retrieval and Social Media Interaction as Stochastic Process (Distinguished Lecture)
University of Duisburg-Essen, Germany –
Stochastic models have a long history in information retrieval (IR). For modeling sequences of interactions, different variants of Markov models have been proposed by a number of researchers. Here a user moves stochastically between different model states, which are not directly observable; instead, some signal is emitted along with each transition. For applying these models to interactive retrieval, we aim at modeling search progress at a level that is comparable to cognitive models. This allows for user-oriented analysis of interactive IR, for user guidance and for stochastic simulations of interactive IR. As a second application domain, we regard social media interaction, focusing on rumor detection and veracity in Twitter streams. Experimental results from both domains show that these approaches are well-suited for dealing with real-world data.
Norbert Fuhr holds a PhD (Dr.) in Computer Science from the Technical University of Darmstadt, which he received in 1986. He became Associate Professor in the computer science department of the University of Dortmund in 1991 and was appointed Full Professor for computer science at the University of Duisburg-Essen in 2002.
His past research dealt with topics such as probabilistic retrieval models, the integration of IR and databases, retrieval in distributed digital libraries and XML documents, and user friendly retrieval interfaces. His current research interests are models for interactive retrieval, user-oriented retrieval methods and social media retrieval.
Norbert Fuhr has served as PC member and program chair of major conferences in IR and digital libraries, and on the editorial boards of several journals in these areas.
In 2012, Norbert Fuhr received the Gerald Salton Award of ACM-SIGIR.
Mário Jorge Costa Gaspar da Silva
Mathematics, Physics & Machine Learning Seminar Series (Online)
The Mathematics, Physics & Machine Learning seminar series has started on October 2020 and runs until March 2021.
The seminars aim to bring together mathematicians and physicists interested in machine learning (ML) with ML and AI experts interested in mathematics and physics, with the goal of introducing innovative Mathematics and Physics-inspired techniques in Machine Learning and, reciprocally, applying Machine Learning to problems in Mathematics and Physics.
Attendance is free but registration is required.
More information is available here.
International European Conference on Parallel and Distributed Computing
The 27th International European Conference on Parallel and Distributed Computing (Euro-Par 2021) will take from August 30 to September 3 2021 in Lisbon.
Euro-Par is the prime European conference covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to full-fledged applications, from architecture, compiler, language and interface design and implementation, to tools, support infrastructures, and application performance aspects.
The 2021 edition of Euro-Par will be organized as a collaboration between INESC-ID and Instituto Superior Técnico (IST).
– Abstract Submission: February 5, 2021
– Paper Submission Deadline: February 12, 2021
– Author Notification: April 30, 2021
– Camera-Ready Papers: June 6, 2021
More information is available here.