TY - GEN
T1 - Exploring Social Influence on Location-Based Social Networks
AU - Wen, Yu Ting
AU - Lei, Po Ruey
AU - Peng, Wen Chih
AU - Zhou, Xiao Fang
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/26
Y1 - 2015/1/26
N2 - Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., The social influence of friends. We propose a new social influence-based user recommender framework (SIR) to discover the potential value from reliable users (i.e., Close friends and travel experts). Explicitly, our SIR framework is able to infer influential users from an LBSN. We claim to capture the interactions among virtual communities, physical mobility activities and time effects to infer the social influence between user pairs. Furthermore, we intend to model the propagation of influence using diffusion-based mechanism. Moreover, we have designed a dynamic fusion framework to integrate the features mined into a united follow probability score. Finally, our SIR framework provides personalized top-k user recommendations for individuals. To evaluate the recommendation results, we have conducted extensive experiments on real datasets (i.e., The Go Walla dataset). The experimental results show that the performance of our SIR framework is better than the state-of the-art user recommendation mechanisms in terms of accuracy and reliability.
AB - Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., The social influence of friends. We propose a new social influence-based user recommender framework (SIR) to discover the potential value from reliable users (i.e., Close friends and travel experts). Explicitly, our SIR framework is able to infer influential users from an LBSN. We claim to capture the interactions among virtual communities, physical mobility activities and time effects to infer the social influence between user pairs. Furthermore, we intend to model the propagation of influence using diffusion-based mechanism. Moreover, we have designed a dynamic fusion framework to integrate the features mined into a united follow probability score. Finally, our SIR framework provides personalized top-k user recommendations for individuals. To evaluate the recommendation results, we have conducted extensive experiments on real datasets (i.e., The Go Walla dataset). The experimental results show that the performance of our SIR framework is better than the state-of the-art user recommendation mechanisms in terms of accuracy and reliability.
UR - http://www.scopus.com/inward/record.url?scp=84936977010&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2014.66
DO - 10.1109/ICDM.2014.66
M3 - Conference contribution
AN - SCOPUS:84936977010
VL - 2015-January
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1043
EP - 1048
BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
A2 - Kumar, Ravi
A2 - Toivonen, Hannu
A2 - Pei, Jian
A2 - Zhexue Huang, Joshua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
Y2 - 14 December 2014 through 17 December 2014
ER -