Preference Mining for Personalised Search on the Social Semantic Web
The main goal of this proposal is to explore and develop suitable techniques for mining preferences for personalised
search on the Social Semantic Web. We especially want to extract preferences from textual documents on the Web (such as blog
posts, recommendations, and reviews) via deep-learning-based techniques for natural language processing, such as deep-learning-based
sentiment analysis. We also want to develop a small preference-mining demonstrator.
EPSRC Doctoral Prize for Oana Tifrea-Marciuska.
Lightweight Tag−Aware Personalized Recommendation on the Social Web Using Ontological Similarity
Zhenghua Xu‚ Oana Tifrea−Marciuska‚ Thomas Lukasiewicz‚ Maria Vanina Martinez‚ Gerardo I. Simari and Cheng Chen
In IEEE Access. 2018.
Ontological Query Answering under Many−Valued Group Preferences in Datalog+⁄−
Bettina Fazzinga‚ Thomas Lukasiewicz‚ Maria Vanina Martinez‚ Gerardo I. Simari and Oana Tifrea−Marciuska
In International Journal of Approximate Reasoning. Vol. 93. Pages 354–371. February, 2018.