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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Nishioka, Chifumi
Große-Bölting, Gregor
Scherp, Ansgar
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Title: Influence of time on user profiling and recommending researchers in social media
Citation: Nishioka C, Große-Bölting G & Scherp A (2015) Influence of time on user profiling and recommending researchers in social media. In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW '15), volume 21-22-October-2015. 15th International Conference on Knowledge Technologies and Data-driven Business, Graz, Austria, 21.10.2015-22.10.2015. New York: ACM, p. Article 9.
Issue Date: 31-Dec-2015
Date Deposited: 22-Oct-2018
Conference Name: 15th International Conference on Knowledge Technologies and Data-driven Business
Conference Dates: 2015-10-21 - 2015-10-22
Conference Location: Graz, Austria
Abstract: We conduct two experiments to compare different scoring functions for extracted user interests and measure the influence of using older data. We apply our experiments in the domains of computer science and medicine. The first experiment assesses similarity scores between a user's social media profile and a corresponding user's publication profile, in order to evaluate to which extend a user's social media profile reflects his or her professional interests. The second experiment recommends related researchers profiled by their publications based on a user's social media profile. The result revealed that while the functions using spreading activation produce large similarity scores between a user profile and publication profile, the scoring functions with statistical methods (e.g., an extension of BM25 with spreading activation) perform best for recommendation. In terms of the temporal influence, the older data have almost no influence on the performance in the medicine dataset. However, in the computer science dataset, while there is a positive influence in the first experiment, the second experiment demonstrated a negative influence when adding too old data.
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