Dr Frank Hopfgartner
- Interactive Information Systems such as information retrieval, knowledge sharing, enterprise search, and recommender systems. Experience in user modelling, gamification, personalized search and recommendation, user-centric evaluation, and other challenges that center around satisfying users’ information needs.
- Content Analysis: Automatic information extraction, indexing, semantic analysis of text documents, analysis of low-level multimedia features, and the application of deep learning methods to classify multimedia content. Manual methods include work on engaging the crowd for data and document annotation and categorization.
- Self-tracking & lifelogging and its potentials for personalization. Interested in the analysis, and visualisation of sensor and lifelogging data. Other challenges pertain processing vast amounts of data and identifying user interests, skills etc. and their usage in real world systems like health or recommendation systems.
Current PhD students
Ahmed Alnuhayt: The Role of Computational Intelligence and Aggregation Systems in Decision Making
Anas Alsuhaibani: Investigating the Role of Social Media in Supporting International Students Transition to Study in the UK
Marc Bonne: Educational games and their impact on mathematics anxiety in University students
Cui Cui: Towards a framework for constructing an open digital library of web archives - Creating a small scale, topic-based digital library of web archives of Chinese studies
Omaima Fallatah: Mapping and aligning large Knowledge Bases
Paula Goodale: Constructing Personal Narratives Through Exploration of Cultural Spaces Online
Ofulue Nchemko: Understanding Motivation and its Impacts on Governance within Crowdsourcing Platforms
Lee Pretlove: The quantified runner: the long-term value and preservation of parkrunners' self-tracking fitness data from the perspectives of the parkrunner, self-tracking device manufacturers and archival institutions
David Walsh: Supporting information access in digital cultural heritage
Zhixue Zhao: Learning from unbalanced data and limited data for automated hate speech detection