Multimedia Tools and Applications

, Volume 78, Issue 21, pp 29867–29880 | Cite as

A novel application recommendation method combining social relationship and trust relationship for future internet of things

  • Chunhua Ju
  • Jie Wang
  • Chonghuan XuEmail author


Traditional collaborative filtering methods always utilize Cosine and Pearson methods to calculate the similarity of users. When the nearest neighbor doesn’t comment the predicted item, then the nearest neighbor has no influence on results, thus affecting the accuracy of collaborative filtering recommendation. And the traditional recommendation systems always have the problems of data sparsity, cold start and so on. In this paper, we consider social relationship and trust relationship, and put forward a novel application recommendation method that combines users’ social relationship and trust relationship. Specifically, we combine social relationship and user preference towards applications to calculate similarity score, we fuse the trust relationship based on familiarity and user reputation to calculate trust score. The final prediction score is calculated by fusing similar relationship and trust relationship properly. And the proposed method can effectively improve accuracy of recommendations.


Big data computing Future internet of things Application recommendation Social relationship Social similarity Trust relationship 



This paper is supported by Zhejiang Public Welfare Technology Applied Research Project (LGN18G010001), National Natural Science Foundation of China (71702164).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of Zhejiang Gongshang UniversityHangzhouChina
  2. 2.Business Administration College of Zhejiang Gongshang UniversityHangzhouChina
  3. 3.Shangmao College of Zhejiang Technical Institute of EconomicsHangzhouChina

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