A Hybrid Social Network-based Collaborative Filtering Method for Personalized Manufacturing Service Recommendation

  • Shuai Zhang Zhejiang University of Finance & Economics, China http://orcid.org/0000-0002-6405-584X
  • Wenting Yang Zhejiang University of Finance & Economics, China
  • Song Xu Zhejiang University of Finance & Economics, China
  • Wenyu Zhang Zhejiang University of Finance & Economics, China

Abstract

Nowadays, social network-based collaborative filtering (CF) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. However, these types of methods are rarely used for recommending manufacturing services. Hence, this study has developed a hybrid social network-based CF method for recommending personalized manufacturing services. The trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. The stochastic approach for link structure analysis (SALSA) was adopted to select top K trustworthy enterprises while also considering their reputation propagation on enterprise social network. The predicted ratings of candidate services were computed by using an extended user-based CF method where the particle swarm optimization (PSO) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. Finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based CF method.

References

[1] Adomavicius G., Tuzhilin A.(2005); Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749, 2005.
https://doi.org/10.1109/TKDE.2005.99

[2] Borodin A., Roberts G.O., Rosenthal J.S. et al. (2001); Finding authorities and hubs from link structures on the world wide web, In Proceedings of the 10th International Conference on World Wide Web, ACM, Hong Kong, China, 415–429, 2001.

[3] Brin S., Page L. (1998); The anatomy of a large-scale hypertextual web search engine, Computer Networks and ISDN Systems, 30(1-7), 107–117, 1998.

[4] Cai M., Zhang W.Y., Zhang K.(2011); ManuHub: A semantic web system for ontologybased service management in distributed manufacturing environments, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(3), 574–582, 2011.
https://doi.org/10.1109/TSMCA.2010.2076395

[5] Colorni A., Dorigo M., Maffioli F., et al. (1986); Heuristics from nature for hard combinatorial optimization problems, International Transactions in Operational Research, 3(1), 1-21, 1986.

[6] Deng S. G., Huang L. T., Xu G. D.(2014); Social network-based service recommendation with trust enhancement, Expert Systems with Applications, 41(18), 8075–8084, 2014.
https://doi.org/10.1016/j.eswa.2014.07.012

[7] Duke A., Davies J., Richardson M. (2005); Enabling a scalable service-oriented architecture with semantic Web Services, BT Technology Journal, 23(3), 191–201, 2005.
https://doi.org/10.1007/s10550-005-0041-2

[8] Eirinaki M., Louta M. D., Varlamis I. (2014); A trust-aware system for personalized user recommendations in social networks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(4), 409-421, 2014.
https://doi.org/10.1109/TSMC.2013.2263128

[9] Esfahani M. T., Torabia S. H., Vahidi B. (2015); A new optimal approach for improvement of active power filter using FPSO for enhancing power quality, International Journal of Electrical Power & Energy Systems, 69, 188-199, 2015.
https://doi.org/10.1016/j.ijepes.2014.12.078

[10] Hu Y. C., Liao P. C. (2011); Finding critical criteria of evaluating electronic service quality of Internet banking using fuzzy multiple-criteria decision making, Applied Soft Computing, 11(4), 3764-3770, 2011.
https://doi.org/10.1016/j.asoc.2011.02.008

[11] Hwang Y. S.(2004); The evolution of alliance formation: an organizational life cycle framework, Diss. Rutgers University, 2004.

[12] Kennedy J., Eberhart R. (1995); Particle swarm optimization, In Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, 1942–1948, 1995.

[13] Kleinberg J. M. (1999); Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM), 46(5), 604–632, 1999.
https://doi.org/10.1145/324133.324140

[14] Langville A. N., Meyer C. D. (2005); A survey of eigenvector methods for web information retrieval, Society for Industrial and Applied Mathematics (SIAM) Review, 47(1), 135–161, 2005.

[15] Lempel R., Moran S. (2000); The stochastic approach for link-structure analysis (SALSA) and the TKC effect, Computer Networks, 33(1-6), 387-401, 2000.
https://doi.org/10.1016/S1389-1286(00)00034-7

[16] Lempel R., Moran S. (2001); SALSA: the stochastic approach for link-structure analysis, ACM Transactions on Information Systems (TOIS), 19(2), 131–160, 2001.
https://doi.org/10.1145/382979.383041

[17] Liu J. T., Wu C. H., Liu W. Y. (2013); Bayesian probabilistic matrix factorization with social relations and item contents for recommendation, Decision Support Systems, 55(3), 838–850, 2013.
https://doi.org/10.1016/j.dss.2013.04.002

[18] Najork M., Gollapudi S., Panigrahy R. (2009); Less is more: sampling the neighborhood graph makes salsa better and faster, Proceedings of the 2th ACM International Conference on Web Search and Data Mining, ACM, Barcelona, Spain, 242–251, 2009.

[19] Park J. B., Jeong Y. W., Shin J. R., et al. (2010); An improved particle swarm optimization for nonconvex economic dispatch problems, IEEE Transactions on Power Systems, 25(1), 156–166, 2010.
https://doi.org/10.1109/TPWRS.2009.2030293

[20] Perugini S., Goncalves M. A., Fox E. A. (2004); Recommender systems research: A connection-centric survey, Journal of Intelligent Information Systems, 23(2), 107–143, 2004.
https://doi.org/10.1023/B:JIIS.0000039532.05533.99

[21] Rahuman M. S. (2012); Improved web link analysis using community based popularity approach, Proc. of the 2th Intl. Conf. on Computing, Communication and Information Technology, Hammamet, Tunisia, 41-44, 2012.

[22] Rodgers J. L., Nicewander W. A.(1988); Thirteen ways to look at the correlation coefficient, The American Statistician, 42(1), 59-66, 1988.

[23] Salakhutdinov R., Mnih A. (2008); Bayesian probabilistic matrix factorization using Markov Chain Monte Carlo, Proc. of the 25th Intl. Conf. on Machine Learning, Helsinki, Finland, 880-887, 2008.

[24] Sobecki J. (2014); Comparison of selected swarm intelligence algorithms in student courses recommendation application, International Journal of Software Engineering and Knowledge Engineering, 24(1), 91-109, 2014.
https://doi.org/10.1142/S0218194014500041

[25] Sun Z. B., Han L. X., Huang W. L., et al. (2015); Recommender systems based on social networks, Journal of Systems and Software, 99, 109-119, 2015.
https://doi.org/10.1016/j.jss.2014.09.019

[26] Tyagi S., Bharadwaj K. K. (2013); Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining, Swarm and Evolutionary Computation, 13, 1-12, 2013.
https://doi.org/10.1016/j.swevo.2013.07.001

[27] Wang Y. J., Yang Y. P. (2009); Particle swarm optimization with preference order ranking for multi-objective optimization, Information Sciences, 179(12), 1944-1959, 2009.
https://doi.org/10.1016/j.ins.2009.01.005

[28] White S., Smyth P. (2003); Algorithms for estimating relative importance in networks, Proc. of the 9th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, Washington D.C., USA, 266-275, 2003.

[29] Zhang W. Y., Zhang S., Chen Y. G., et al. (2013); Combining social network and collaborative filtering for personalised manufacturing service recommendation, International Journal of Production Research, 51(22), 6702-6719, 2013.
https://doi.org/10.1080/00207543.2013.832839

[30] Zhu Y., Zhang S., Wang Y., et al. (2013); A social network-based expertise-enhanced collaborative filtering method for e-government service recommendation, Advances in Information Sciences and Service Sciences, 5(10), 724-735, 2013.
https://doi.org/10.4156/aiss.vol5.issue10.85
Published
2017-09-10
How to Cite
ZHANG, Shuai et al. A Hybrid Social Network-based Collaborative Filtering Method for Personalized Manufacturing Service Recommendation. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 5, p. 728-740, sep. 2017. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2930>. Date accessed: 30 nov. 2020. doi: https://doi.org/10.15837/ijccc.2017.5.2930.

Keywords

manufacturing service recommendation, social network, collaborative filtering, SALSA, PSO