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Social recommendation model combining trust propagation and sequential behaviors

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Abstract

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.

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Acknowledgments

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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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China

    Zhijun Zhang

  2. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Zhijun Zhang & Hong Liu

  3. Shandong Provincial Key Laboratory for Novel Distributed Computer Software, Jinan, 250014, China

    Zhijun Zhang & Hong Liu

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  1. Zhijun Zhang
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  2. Hong Liu
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Correspondence to Zhijun Zhang.

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Zhang, Z., Liu, H. Social recommendation model combining trust propagation and sequential behaviors. Appl Intell 43, 695–706 (2015). https://doi.org/10.1007/s10489-015-0681-y

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  • DOI: https://doi.org/10.1007/s10489-015-0681-y

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