Group Recommendation Robotics Based on External Social-Trust Networks

  • Guang Fang
  • Lei Su
  • Di Jiang
  • Liping Wu
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Recommendation robotics helps users to find similar interests or purposes to those of others. We often provide advice to close friends or similar users, such as sharing favorite dishes, listening to favorite music, etc. In traditional group recommendation robotics, however, users’ personalities have been ignored. In this chapter, a method of group recommendation robotics based on social-trust networks is proposed, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members inside and outside of the group. We employ a collaborative filter to obtain members’ predictions and adjust the final group preference rating by the external social-trust network if the group has a large disagreement. The experimental results show that the proposed method has a lower root mean square error and leads to a satisfactory effect for the group.


Social network Recommendation robotics Group recommendation robotics 



This work is supported by the National Science Foundation of China under the Grant No.61365010.


  1. 1.
    Amer-Yahia, S., Roy, S. B., Chawlat, A., Das, G., & Yu, C. (2009). Group recommendation: Semantics and efficiency. Proceedings of the VLDB Endowment, 2(1), 754–765.CrossRefGoogle Scholar
  2. 2.
    Baltrunas, L., Makcinskas, T., & Ricci, F. (2010). Group recommendations with rank aggregation and collaborative filtering. In ACM Conference on Recommender Systems (pp. 119–126). New York, NY: ACM.Google Scholar
  3. 3.
    Berkovsky, S., & Freyne, J. (2010). Group-based recipe recommendations: Analysis of data aggregation strategies. In ACM Conference on Recommender Systems (pp. 111–118). New York, NY: ACM.Google Scholar
  4. 4.
    Christensen, I. A., & Schiaffino, S. (2014). Social influence in group recommender systems. Online Information Review, 38(4), 5–5.Google Scholar
  5. 5.
    Dyer, J. S., & Sarin, R. K. (2011). Group preference aggregation rules based on strength of preference. Management Science, 25(9), 822–832.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., et al. (2010). Enhancing group recommendation by incorporating social relationship interactions. In International ACM Siggroup Conference on Supporting Group Work, Group 2010, Sanibel Island, Florida, USA, November (pp. 97–106). New York, NY: ACM.Google Scholar
  7. 7.
    Guo, G., Zhang, J., & Yorke-Smith, N. (2013). A novel Bayesian similarity measure for recommender systems. In International Joint Conference on Artificial Intelligence (pp. 2619–2625). Menlo Park, CA: AAAI Press.Google Scholar
  8. 8.
    Jameson, A. (2004). More than the sum of its members: Challenges for group recommender systems. In Working Conference on Advanced Visual Interfaces (pp. 48–54). New York, NY: ACM.CrossRefGoogle Scholar
  9. 9.
    Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The Adaptive Web. Lecture Notes in Computer Science (pp. 596–627). Berlin: Springer.Google Scholar
  10. 10.
    Jones, J. E. (1976). Thomas-Kilmann conflict mode instrument. Group & Organization Management, 1(2), 249–251.Google Scholar
  11. 11.
    Kelleher, J., & Bridge, D. (2004). An accurate and scalable collaborative recommender. Dordrecht: Kluwer Academic PublisherszbMATHCrossRefGoogle Scholar
  12. 12.
    Kim, J. K., Kim, H. K., Oh, H. Y., & Ryu, Y. U. (2010). A group recommendation system for online communities. International Journal of Information Management, 30(3), 212–219.CrossRefGoogle Scholar
  13. 13.
    Kompan, M., & Bielikova, M. (2014). Group recommendations: Survey and perspectives. Computing & Informatics, 33(2), 1–31.Google Scholar
  14. 14.
    Linden, G., Smith, B., & York, J. (2003). recommendations. IEEE Internet Computing. January–February, 4(1), 76–80.CrossRefGoogle Scholar
  15. 15.
    Lu, H., Li, B., Zhu, J., Li, Y., Li, Y., Xu, X., et al. (2017). Wound intensity correction and segmentation with convolutional neural networks. Concurrency & Computation Practice & Experience, 29(6), e3927.CrossRefGoogle Scholar
  16. 16.
    Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., & Serikawa, S. (2017). Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet of Things Journal, PP(99), 1–1.Google Scholar
  17. 17.
    Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks & Applications, 23(2), 368–375.CrossRefGoogle Scholar
  18. 18.
    Najjar, N. A. & Wilson, D. C. (2014). Differential neighborhood selection in memory-based group recommender systems. In The International Conference of the Florida Artificial Intelligence Research Society.Google Scholar
  19. 19.
    Ortega, F., Bobadilla, J., Hernando, A., & Gutiérrez, A. (2013). Incorporating group recommendations to recommender systems: Alternatives and performance. Information Processing & Management, 49(4), 895–901.CrossRefGoogle Scholar
  20. 20.
    Quijano-Sanchez, L., Recio-Garcia, J. A., & Diaz-Agudo, B. (2010). Personality and social trust in group recommendations. In IEEE International Conference on Tools with Artificial Intelligence (pp. 121–126). Piscataway: IEEE.Google Scholar
  21. 21.
    Quijano-Sanchez, L., Reciogarcia, J., & Diazagudo, B. (2011). Group recommendation methods for social network environments. In Proceedings of the Recommender System Social Web (p. 24).Google Scholar
  22. 22.
    Quijano-Sanchez, L., Recio-Garcia, J. A., Diaz-Agudo, B., & Jimenez-Diaz, G. (2013). Social factors in group recommender systems. ACM Transactions on Intelligent Systems & Technology, 4(1), 8.CrossRefGoogle Scholar
  23. 23.
    Quijano-Sánchez, L., Díaz-Agudo, B., & Recio-García, J. A. (2014). Development of a group recommender application in a social network. Knowledge-Based Systems, 71, 72–85.CrossRefGoogle Scholar
  24. 24.
    Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (2011). Recommender systems handbook (pp. 1–35). Berlin: Springer.zbMATHCrossRefGoogle Scholar
  25. 25.
    Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In International Conference on World Wide Web (pp. 285–295).Google Scholar
  26. 26.
    Schafer, J. B., Dan, F., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. Berlin: Springer.CrossRefGoogle Scholar
  27. 27.
    Shin, S., Jang, S. J., & Lee, S. P. (2011). The user-group based recommendation for the diverse multimedia contents in the social network environments. In IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (pp. 202–206).Google Scholar
  28. 28.
    Masthoff, J. (2004). Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction, 14(1), 37–85.CrossRefGoogle Scholar
  29. 29.
    Xu, X., He, L., Lu, H., Gao, L., & Ji, Y. (2018). Deep adversarial metric learning for cross-modal retrieval. World Wide Web-internet & Web Information Systems (pp. 1–16).Google Scholar
  30. 30.
    Zhang, Y. (2016). Grorec: A group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Transactions on Services Computing, 9(5), 786–795.CrossRefGoogle Scholar
  31. 31.
    Zhang, Y., Zhang, D., Hassan, M. M., Alamri, A., & Peng, L. (2015). Cadre: Cloud-assisted drug recommendation service for online pharmacies. Mobile Networks & Applications, 20(3), 348–355.CrossRefGoogle Scholar
  32. 32.
    Zhang, Y., Chen, M., Huang, D., Wu, D., & Li, Y. (2016). idoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Computer Systems, 66, 30–35.CrossRefGoogle Scholar
  33. 33.
    Zhang, Y., Tu, Z., & Wang, Q. (2017). TempoRec: Temporal-topic based recommender for social network services. Mobile Networks & Applications, 1–10.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Guang Fang
    • 1
  • Lei Su
    • 1
  • Di Jiang
    • 1
  • Liping Wu
    • 1
  1. 1.Kunming University of Science and TechnologyKunmingChina

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