Abstract
The revenue of an e-commerce system is affected directly by the prediction accuracy of recommendation system. Although recommendation systems have been comprehensively analyzed in the past decade, the study of social-based recommendation systems just started. In this paper, aiming at providing a general method for improving recommendation systems by incorporating social network information, we propose a social recommendation algorithm based on stochastic gradient matrix decomposition in social network so as to improve the prediction accuracy. This paper considered the social network as auxiliary information, and proposed a matrix factorization based on social recommendation algorithm, which systematically illustrate how to design a matrix factorization objective function with social regularization. It constructed a matrix with the social network and the user scoring matrix, and proposed a stochastic gradient descent algorithm for matrix factorization. The empirical analysis on two large datasets demonstrates our proposed algorithm has lower prediction error, and is obviously better than other state-of-the-art methods.
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Agarwal D, Chen B-C (2010) fLDA: matrix factorization through latent dirichlet allocation. In: Proc. of WSDM2010, New York, New York, USA, pp 91–100
Bedi P, Kaur H, Marwaha S (2007) Trust based recommender system for semantic web. In Proc. of IJCAI, pp 2677–2682, 2007
Bellogín A, Castells P, Cantador I (2013) Improving memory-based collaborative filtering by neighbour selection based on user preference overlap. In: Proceedings of the 10th conference on open research areas in information retrieval, pp 145–148
Golbeck J (2005) Computing and applying trust in web-based social networks. PhD thesis, University of Maryland College Park
Hailing X, Xiao W, Xiaodong L et al (2009) Research on internet recommendation system. J Softw 20(2):350–362
Hsu FM, Lin YT, Ho TK (2012) Design and implementation of an intelligent recommendation system for tourist attractions: the integration of EBM model, Bayesian network and Google Maps. Expert Syst Appl 39(3):3257–3264
Huang Chuanguang Y, Jian W, Jing et al (2010) Research on collaborative filtering recommendation algorithm for indefinite neighbors. Comput Sci 33(8):1369–1377
Jamali M, Ester M (2009) TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 397–406
Ma H, Yang H, Lyu MR et al (2008) Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on information and knowledge management, ACM, pp 931–940
Ma H, King I, Lyu MR (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. ACM, pp 203–210
Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on recommender systems, ACM, pp 17–24
Park DH, Kim HK, Choi IY et al (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072
Sarwar B, Karypis G, Konstan J et al (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, ACM, pp 285–295
Sattarpour T, Nazarpour D, Golshannavaz S et al (2018) A multi-objective hybrid GA and TOPSIS approach for sizing and siting of DG and RTU in smart distribution grids. J Ambient Intell Human Comput 9:105. https://doi.org/10.1007/s12652-016-0418-8
Tsai CF, Hung C (2012) Cluster ensembles in collaborative filtering recommendation. Appl Soft Comput 12(4):1417–1425
Tu D, Shu C, Yu H (2013) Context advertisement algorithm based on joint probability matrix decomposition. J Softw 24(3):454–464
Wang L, Meng X, Zhang Y (2012) Context perception recommendation system. J Softw 23(1):103–115
Xia KJ, Wang JQ (2017) A novel medical image enhancement algorithm based on improvement correction strategy in wavelet transform domain. Clust Comput. https://doi.org/10.1007/s10586-017-1264-y
Xia KJ, Wang JQ, Wu Y (2017) Robust Alzheimer disease classification based on feature integration fusion model for magnetic. J Med Imaging Health Inf 7:1–6
Xia KJ, Yin HS, Wang JQ (2018) A novel improved deep convolutional neural network model for medical image fusion. Clust Comput 3:1–13
Zhang Y, Zhang M, Liu Y et al (2013) Localized matrix factorization for recommendation based on matrix block diagonal forms. In: Proceedings of the 22nd international conference on world wide web, pp 1511–1520
Zhou T, Shan H, Banerjee A et al (2012) Kernelized probabilistic matrix factorization: exploiting graphs and side information. In: Proceedings of the 2012 SIAM international conference on data mining (SDM), vol 36. pp 403–414
Acknowledgements
The Scientific and Technological Research Program of Henan Province, China under Grant no. 172102210111. The Ministry of Public Security Technical Research Plan, under Grant no. 2016JSYJB38. The Scientific and Technological Research Program of Henan Province, China under Grant no. 172102210441.
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Zhang, Tw., Li, Wp., Wang, L. et al. Social recommendation algorithm based on stochastic gradient matrix decomposition in social network. J Ambient Intell Human Comput 11, 601–608 (2020). https://doi.org/10.1007/s12652-018-1167-7
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DOI: https://doi.org/10.1007/s12652-018-1167-7