Integrated collaborative filtering recommendation in social cyber-physical systems

International Journal of Distributed Sensor Networks

Volumn 13, Issue 12, 2017, Pages

  Xu, Jiachen ^a   Liu, Anfeng ^a   Xiong, Naixue ^a,b   Wang, Tian ^c   Zuo, Zhengbang ^d  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b Colorado Technical University ^*  (United States)

^c HUAQIAO UNIVERSITY   (China)

^d HUNAN NORMAL UNIVERSITY   (China)

Author keywords

integrated collaborative filtering recommendation; integrated collaborative filtering recommendation approach; recommendation performance; Social cyber physical systems; trust

Indexed keywords

BIG DATA; CYBER PHYSICAL SYSTEM; DATA MINING; DISTRIBUTED COMPUTER SYSTEMS; EMBEDDED SYSTEMS; ERRORS; INFORMATION FILTERING; MEAN SQUARE ERROR;

COLLABORATIVE FILTERING RECOMMENDATIONS; CONCRETE PROCESS; E-COMMERCE WEBSITES; MEAN ABSOLUTE ERROR; NEAREST NEIGHBORS; RECOMMENDATION PERFORMANCE; ROOT MEAN SQUARE ERRORS; TRUST;

COLLABORATIVE FILTERING;

EID: 85039919350     PISSN: 15501329     EISSN: 15501477     Source Type: Journal    
DOI: 10.1177/1550147717749745     Document Type: Article

References (50)

1. Chen Z, Liu A, Li Z. Distributed duty cycle control for delay improvement in wireless sensor networks. Peer Peer Netw Appl 2017; 10(3): 559–578.
2. Zhang Q, Liu A., An unequal redundancy level-based mechanism for reliable data collection in wireless sensor networks. EURASIP J Wirel Comm 2016; 2016: 258.
3. Xu Y, Liu A, Huang C., Delay-aware program codes dissemination scheme in internet of everything. Mob Inf Syst 2016; 2016: 2436074.
4. Huang C, Ma M, Liu Y. Preserving source location privacy for energy harvesting WSNs. Sensors 2017; 17(4): E724.
5. Chen Z, Liu A, Li Z. Energy-efficient broadcasting scheme for smart industrial wireless sensor networks. Mob Inf Syst 2017; 2017: 7538190.
6. Liu X, Dong M, Ota K. Trace malicious source to guarantee cyber security for mass monitor critical infrastructure. J Comput Syst Sci. Epub ahead of print 8 October 2016. DOI: 10.1016/j.jcss.2016.09.008.
7. Li T, Liu A, Huang C., A similarity scenario-based recommendation model with small disturbances for unknown items in social networks. IEEE Access 2016; 4: 9251–9272.
8. Liu A, Zhang Q, Li Z. A green and reliable communication modeling for industrial internet of things. Comput Electr Eng 2017; 58: 364–381.
9. Liu X, Liu A, Li Z. Distributed cooperative communication nodes control and optimization reliability for resource-constrained WSNs. Neurocomputing 2017; 270: 122–136.
10. Xu Y, Chen X, Liu A. A latency and coverage optimized data collection scheme for smart cities based on vehicular ad-hoc networks. Sensors 2017; 17: 888.
11. Liu Y, Liu A, Li Y. APMD: a fast data transmission protocol with reliability guarantee for pervasive sensing data communication. Pervasive Mob Comput 2017; 41: 413–435.
12. Liu YX, Liu A, Guo S. Context-aware collect data with energy efficient in Cyber–physical cloud systems. Future Gener Comp Sy. Epub ahead of print 7 June 2017. DOI: 10.1016/j.future.2017.05.029.
13. Liu Q, Liu A., On the hybrid using of unicast-broadcast in wireless sensor networks. Comput Electr Eng. Epub ahead of print 24 March 2017. DOI: 10.1016/j.compeleceng.2017.03.004.
14. Liu X, Li G, Zhang S. Big program code dissemination scheme for emergency software-define wireless sensor networks. Peer Peer Netw Appl. Epub ahead of print 23 May 2017. DOI: 10.1007/s12083-017-0565-5.
15. Wang J, Liu A, Yan T. A resource allocation model based on double-sided combinational auctions for transparent computing. Peer Peer Netw Appl. Epub ahead of print 10 April 2017. DOI: 10.1007/s12083-017-0556-6.
16. Liu A, Liu X, Tang Z. Preserving smart sink location privacy with delay guaranteed routing scheme for WSNs. ACM T Embed Comput S 2017; 16: 68.
17. Liu A, Liu X, Wei T. Distributed multi-representative re-fusion approach for heterogeneous sensing data collection. ACM T Embed Comput S 2017; 16: 73.
18. Chen X, Xu Y, Liu A., Cross layer design for optimizing transmission reliability, energy efficiency, and lifetime in body sensor networks. Sensors 2017; 17(4): E900.
19. Wang J, Liu A, Zhang S., Key parameters decision for cloud computing: insights from a multiple game model. Concurr Comp: Pract E 2017; 29: e4200.
20. Liu X, Liu Y, Song H. Big data orchestration as a service network. IEEE Commun Mag 2017; 55(9): 94–101.
21. Liu A, Chen Z, Xiong NN., An adaptive virtual relaying set scheme for loss-and-delay sensitive WSNs. Inform Sciences 2018; 424: 118–136.
22. Li T, Liu Y, Gao L. A cooperative-based model for smart-sensing tasks in fog computing. IEEE Access 2017; 5: 21296–21311.
23. Chen Z, Ma M, Liu X. Reliability improved cooperative communication over wireless sensor networks. Symmetry 2017; 9(10): 209.
24. Chen X, Ma M, Liu A., Dynamic power management and adaptive packet size selection for IoT in e-Healthcare. Comput Electr Eng. Epub ahead of print 16 June 2017. DOI: 10.1016/j.compeleceng.2017.06.010.
25. Goldberg D., Using collaborative filtering to weave an information tapestry. Commun ACM 1992; 35(12): 61–70.
26. Konstan JA, Miller BN, Maltz D. GroupLens: applying collaborative filtering to Usenet news. Commun ACM 1997; 40(3): 77–87.
27. Resnick P, Iacovou N, Suchak M. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work (CSCW ’94), Chapel Hill, NC, 22–26 October 1994, pp.175–186. New York: ACM.
28. Linden G, Smith B, York J., Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 2003; 7(1): 76–80.
29. Sarwar B, Karypis G, Konstan J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web (WWW ’01), Hong Kong, 1–5 May 2001, pp.285–295. New York: ACM.
30. Zhao ZD, Shang M. User-based collaborative-filtering recommendation algorithms on Hadoop. In: Proceedings of the 3rd international conference on knowledge discovery and data mining, 2010 (WKDD ’10), Phuket, Thailand, 9–10 January 2010, pp.478–481. New York: IEEE.
31. Chen H, Li X, Huang Z., Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries, 2005 (JCDL ‘05), Denver, CO, 7–11 June 2005, pp.141–142. New York: IEEE.
32. Huang Z, Chen H, Zeng D., Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM T Inform Syst 2004; 22(1): 116–142.
33. Goldberg DS, Roth FP., Assessing experimentally derived interactions in a small world. P Natl Acad Sci USA 2003; 100(8): 4372–4376.
34. Liben-Nowell D, Kleinberg J. The link prediction problem for social networks. In: Proceedings of the 12th international conference on information and knowledge management, New Orleans, LA, 3–8 November 2003, pp.556–559. New York: ACM.
35. Popescul A, Ungar LH. Statistical relational learning for link prediction. In: Proceedings of the workshop on learning statistical models from relational data, Acapulco, Mexico, 9–15 August 2003. Morgan Kaufmann.
36. Kautz H, Selman B, Shah M., Referral Web: combining social networks and collaborative filtering. Commun ACM 1997; 40(3): 63–65.
37. He J, Chu WW., A social network-based recommender system (SNRS). In: Memon N, Xu J, Hicks D. (eds) Data mining for social network data, vol. 12 (Annals of information systems). Boston, MA: Springer, 2010, pp.47–74.
38. Zhang G, Sun Y, Xu M. Weibo clustering: a new approach utilizing users’ reposting data in social networking services. Comput Sci Inf Syst 2014; 11(3): 1157–1172.
39. Wang Y, Yin G, Cai Z. A trust-based probabilistic recommendation model for social networks. J Netw Comput Appl 2015; 55: 59–67.
40. Wang Y, Cai Z, Yin G. A trust measurement in social networks based on game theory. In: Proceedings of the international conference on computational social networks, Beijing, China, 4–6 August 2015, pp.236–247. Cham: Springer.
41. Sherchan W, Nepal S, Paris C., A survey of trust in social networks. ACM Comput Surv 2013; 45(4): 1–33.
42. Eirinaki M, Louta MD, Varlamis I., A trust-aware system for personalized user recommendations in social networks. IEEE T Syst Man Cy A 2013; 44(4): 409–421.
43. Shambour Q, Lu J., A trust-semantic fusion-based recommendation approach for e-business applications. Decis Support Syst 2012; 54(1): 768–780.
44. Ma H, King I, Lyu MR., Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval (SIGIR ’09), Boston, MA, 19–23 July 2009, pp.203–210. New York: ACM.
45. Andersen R, Borgs C, Chayes J. Trust-based recommendation systems: an axiomatic approach. In: Proceedings of the 17th international conference on World Wide Web (WWW ’08), Beijing, China, 21–25 April 2008, pp.199–208. New York: ACM.
46. Deshpande M, Karypis G., Item-based top-N recommendation algorithms. ACM T Inform Syst 2004; 22(1): 143–177.
47. Gao M, Wu Z, Jiang F., Userrank for item-based collaborative filtering recommendation. Inform Process Lett 2011; 111(9): 440–446.
48. Jamali M, Ester M., 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 (KDD ’09), Paris, 28 June–1 July 2009, pp.397–406. New York: ACM.
49. Wang J, de Vries AP, Reinders MJT. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR ’06), Seattle, WA, 6–11 August 2006, pp.501–508. New York: ACM.
50. Lee WP, Ma CY., Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks. Knowl-Based Syst 2016; 106: 125–134.