Title
Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation.
Abstract
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.
Year
DOI
Venue
2018
10.1145/3269206.3271725
CIKM
Keywords
Field
DocType
Social Recommender Systems, Implicit Friends, Heterogeneous Networks, Social Networks
Recommender system,Social relation,Embedding,Social network,Information retrieval,Computer science,Intuition,Network topology,Heterogeneous network,Feature learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6014-2
13
0.57
References 
Authors
38
5
Name
Order
Citations
PageRank
Junliang Yu15410.05
Min Gao21119.52
Jundong Li370950.13
Hongzhi Yin4136475.83
Huan Liu512695741.34