Applied Intelligence

, Volume 43, Issue 3, pp 695–706 | Cite as

Social recommendation model combining trust propagation and sequential behaviors

  • Zhijun ZhangEmail author
  • Hong Liu


All types of recommender systems have been thoroughly explored and developed in industry and academia with the advent of online social networks. However, current studies ignore the trust relationships among users and the time sequence among items, which may affect the quality of recommendations. Three crucial challenges of recommender system are prediction quality, scalability, and data sparsity. In this paper, we explore a model-based approach for recommendation in social networks which employs matrix factorization techniques. Advancing previous work, we incorporate the mechanism of temporal information and trust relations into the model. Specifically, our method utilizes shared latent feature space to constrain the objective function, as well as considers the influence of time and user trust relations simultaneously. Experimental results on the public domain dataset show that our approach performs better than state-of-the-art methods, particularly for cold-start users. Moreover, the complexity analysis indicates that our approach can be easily extended to large datasets.


Recommender system Social network Trust relation Temporal information Probability matrix factorization Social recommendation 



This paper is supported by the National Natural Science Foundation of China (No. 61272094), Natural Science Foundation of Shandong Province (ZR2010QL01, ZR2012GQ010), Science and Technology Development Planning of Shandong Province (2014GGX101011), A Project of Shandong Province Higher Educational Science and Technology Program (J12LN31, J13LN11), Jinan Higher Educational Innovation Plan (201401214, 201303001) and Shandong Provincial Key Laboratory Project.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong Jianzhu UniversityJinanChina
  2. 2.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  3. 3.Shandong Provincial Key Laboratory for Novel Distributed Computer SoftwareJinanChina

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