Time-aware IoE service recommendation on sparse data

Lianyong Qi*, Xiaolong Xu, Wanchun Dou, Jiguo Yu, Zhili Zhou, Xuyun Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)
13 Downloads (Pure)


With the advent of "Internet of Everything" (IoE) age, an excessive number of IoE services are emerging on the web, which places a heavy burden on the service selection decision of target users. In this situation, various recommendation techniques are introduced to alleviate the burden, for example, Collaborative Filtering- (CF-) based recommendation. Generally, CF-based recommendation approaches utilize similar friends or similar services to achieve the recommendation goal. However, due to the sparsity of user feedback, it is possible that a target user has no similar friends and similar services; in this situation, traditional CF-based approaches fail to produce a satisfying recommendation result. Besides, recommendation accuracywould be decreased if time factor is overlooked, as IoE service quality often varies with time. In view of these challenges, a time-aware service recommendation approach named Ser_Rectime is proposed in this paper. Concretely, we first calculate the time-aware user similarity; afterwards, indirect friends of the target user are inferred by Social Balance Theory (e.g., "enemy's enemy is a friend" rule); finally, the services preferred by indirect friends of the target user are recommended to the target user. At last, through a set of experiments deployed on dataset WS-DREAM, we validate the feasibility of our proposal.

Original languageEnglish
Article number4397061
Pages (from-to)1-12
Number of pages12
JournalMobile Information Systems
Publication statusPublished - 2016
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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