Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Social technologies tend to attract research on social structure or interaction. In this paper we analyze the individual use of a social technology, specifically an enterprise people-tagging application. We focus on active participants of the system and distinguish between users who initiate activity and those who respond to activity. This distinction is situated within the preferential attachment theory in order to examine which type of participant contributes more to the process of tagging. We analyze the usage of the people-tagging application in a snapshot representing 3 years of activity, focusing on self-tagging compared to tagging by and of others. The main findings are: (1) People who tag themselves are the most productive contributors to the system. (2) Preferential attachment saturation is reached at 12-14 tags per user. (3) The nature of participation is more significant than the number of participants for system growth. The paper concludes with theoretical and practical implications. © 2011 ASIS&T.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Pavel Klavík, A. Cristiano I. Malossi, et al.
Philos. Trans. R. Soc. A
Ken C.L. Wong, Satyananda Kashyap, et al.
Pattern Recognition Letters