TY - GEN
T1 - Adaptive implicit friends identification over heterogeneous network for social recommendation
AU - Yu, Junliang
AU - Gao, Min
AU - Li, Jundong
AU - Yin, Hongzhi
AU - Liu, Huan
N1 - Funding Information:
This research is supported by National Science Foundation (NSF) under grant number 1614576, the Graduate Scientific Research and Innovation Foundation of Chongqing under grant number cys17035 and China Scholarship Council.
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/17
Y1 - 2018/10/17
N2 - The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation.
AB - The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation.
KW - Heterogeneous Networks
KW - Implicit Friends
KW - Social Networks
KW - Social Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=85058039991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058039991&partnerID=8YFLogxK
U2 - 10.1145/3269206.3271725
DO - 10.1145/3269206.3271725
M3 - Conference contribution
AN - SCOPUS:85058039991
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 357
EP - 366
BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
A2 - Paton, Norman
A2 - Candan, Selcuk
A2 - Wang, Haixun
A2 - Allan, James
A2 - Agrawal, Rakesh
A2 - Labrinidis, Alexandros
A2 - Cuzzocrea, Alfredo
A2 - Zaki, Mohammed
A2 - Srivastava, Divesh
A2 - Broder, Andrei
A2 - Schuster, Assaf
PB - Association for Computing Machinery
T2 - 27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Y2 - 22 October 2018 through 26 October 2018
ER -