This year, the ACM Hypertext conference takes place from July 10 – 13, 2016 in Halifax, Canada. It has been a premium venue for high quality peer-reviewed research on hypertext theory, social systems and applications since 1987 and thus, is a great opportunity to present Know-Center research there.
In the first paper titled “The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems” the authors Dominik Kowald and Elisabeth Lex provide an in-depth analysis of the underlying factors (i.e., frequency, recency and semantic context) of our cognitive-inspired tag recommender algorithm developed in the Learning Layers European Project and how these factors correlate with the given tagging system in order to choose the right method for the right setting.
The second paper with the title “High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Reviews”, authored by Emanuel Lacic, Dominik Kowald and Elisabeth Lex, shows how various rating features and textual features (e.g., the sentiment or contained topics) can be used to explain and predict user satisfaction using the machine learning framework Weka. Although, the authors used a dataset from the airline domain for this paper, the described methods can be utilized for many domains.
We congratulate all authors to this great success!
Complete bibliography:
Kowald, D. & Lex, E. The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems. In Proceedings of the 27th ACM Conference on Hypertext and Social Media. ACM. 2016
The paper is available for download here.
Lacic, E., Kowald, D. & Lex, E. High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Reviews. In Proceedings of the 27th ACM Conference on Hypertext and Social Media. ACM. 2016.
The paper is available for download here.