TY - GEN
T1 - Personalized mention probabilistic ranking — Recommendation on mention behavior of heterogeneous social network
AU - Li, Quanle
AU - Song, Dandan
AU - Liao, Lejian
AU - Liu, Li
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Selecting a suitable person to mention on the Micro-blogging network, expressed as “@username”, is a new aspect of recommendation system which carries great importance to promote user experience and information propagation. We comprehend information propagation as the reach, vitality, and effectiveness of tweet messages. In this case, we consider this mention recommendation as a probabilistic problem and propose our method named Personalized Mention Probabilistic Ranking to find out who has the maximal capability and possibility to help tweet diffusion by utilizing probabilistic factor graph model in the heterogeneous social network. A wide range of features are extracted and highlighted in our model, such as tag similarity, text similarity, social influence, interaction history and named entities. Experimental results show that our approach outperforms the state-of-art algorithms.
AB - Selecting a suitable person to mention on the Micro-blogging network, expressed as “@username”, is a new aspect of recommendation system which carries great importance to promote user experience and information propagation. We comprehend information propagation as the reach, vitality, and effectiveness of tweet messages. In this case, we consider this mention recommendation as a probabilistic problem and propose our method named Personalized Mention Probabilistic Ranking to find out who has the maximal capability and possibility to help tweet diffusion by utilizing probabilistic factor graph model in the heterogeneous social network. A wide range of features are extracted and highlighted in our model, such as tag similarity, text similarity, social influence, interaction history and named entities. Experimental results show that our approach outperforms the state-of-art algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84950317454&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23531-8_4
DO - 10.1007/978-3-319-23531-8_4
M3 - Conference contribution
AN - SCOPUS:84950317454
SN - 9783319235301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 52
BT - Web-Age Information Management - WAIM 2015 International Workshops
A2 - Xiao, Xiaokui
A2 - Zhang, Zhenjie
PB - Springer Verlag
T2 - International Conference on Web-Age Information Management, WAIM 2015 and International Workshop on Heterogeneous Information Network Analysis and Applications, HENA 2015, 2nd International Workshop on Human Aspects of Making Recommendations in and for Social Ubiquitous Networking Environments, HRSUNE 2015
Y2 - 8 June 2015 through 10 June 2015
ER -