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

Shuai Zhang, Wenting Yang, Song Xu, Wenyu Zhang


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.


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

Full Text:



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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

Kleinberg J. M. (1999); Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM), 46(5), 604–632, 1999.

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.

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.

Lempel R., Moran S. (2001); SALSA: the stochastic approach for link-structure analysis, ACM Transactions on Information Systems (TOIS), 19(2), 131–160, 2001.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

DOI: https://doi.org/10.15837/ijccc.2017.5.2930

Copyright (c) 2017 Shuai Zhang, Wenting Yang, Song Xu, Wenyu Zhang

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

IJCCC is an Open Access Journal : CC-BY-NC.

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of CC-BY-NC  License for Website User]

INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

Ethics: This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006); JCR2018: IF=1.585..

IJCCC is indexed in Scopus from 2008 (CiteScore2018 = 1.56):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.


 Impact Factor in JCR2018 (Clarivate Analytics/SCI Expanded/ISI Web of Science): IF=1.585 (Q3). Scopus: CiteScore2018=1.56 (Q2);

SCImago Journal & Country Rank

Editors-in-Chief: Ioan DZITAC & Florin Gheorghe FILIP.