Abstract
Social contexts play critical roles when people provide and receive recommendations in the real world. However, existing recommender mechanisms, such as collaborative filtering, model social connections based on the similarity of users’ interests, without fully considering their social ties at different strengths. Such mechanisms may fail to provide recommendations that fit human needs, ignoring important social relations such as trust and giving rise to so-called “filter bubbles” problems. In this paper, we propose a network-based model to represent multi-faceted relations of actors in local communities, which we call Living Trust Networks (LTNs), and discuss methods to create and manage LTNs based on different types of sensor data including location and proximity information. We also discuss a social recommendation model that can treat LTNs and “user-item interactions” in an integrated manner based on graph neural networks (GNNs). Finally, we present a study of the use of proximity-based LTNs to support people to obtain useful information in a university campus, and discuss the implications of our approach on the design of effective socially-aware recommendation environments based on LTNs.
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References
Sasao, T., Konomi, S., Kostakos, V., Kuribayashi, K., Goncalves, J.: Community reminder: participatory contextual reminder environments for local communities. Int. J. Hum.-Comput. Stud. 102, 41–53 (2017). https://doi.org/10.1016/j.ijhcs.2016.09.001
Burrell, J., Gay, G.K., Kubo, K., Farina, N.: Context-aware computing: a test case. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 1–15. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45809-3_1
Park, S., Kim, Y., Lee, U., Ackerman, M.: Understanding localness of knowledge sharing: a study of Naver KiN‘here’. In: Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices & Services (MobileHCI 2014), pp. 13–22. (2014). https://doi.org/10.1145/2628363.2628407
Horowitz, D., Kamvar, S.D.: The anatomy of a large-scale social search engine. In: Proceedings of the 19th International Conference on World Wide Web (WWW 2010), pp 431–440. Association for Computing Machinery, New York, NY, USA (2010)
Hu, X., Konomi, S.: QFami: an integrated environment for recommending answerers on campus. In: Stephanidis, C., Antona, M., Ntoa, S. (eds.) HCII 2021. CCIS, vol. 1498, pp. 119–125. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90176-9_17
Choudhury, T., Pentland, A.: Sensing and modeling human networks using the sociometer. In: Proceedings of the 7th IEEE International Symposium on Wearable Computers, White Plains, NY, USA, vol. 2003, pp. 216–222. IEEE (2003). https://doi.org/10.1109/ISWC.2003.1241414
Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquitous Comput. 10, 255–268 (2006)
Saquib, N., Bose, A., George, D., Kamvar, S.: Sensei: sensing educational interaction. Proc. ACM Interac. Mob. Wearable Ubiquitous Technol. 1(4), 1–27 (2018)
Gashi, S., Di Lascio, E., Santini, S.: Using unobtrusive wearable sensors to measure the physiological synchrony between presenters and audience members. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, no. 1, pp. 1–19. Association for Computing Machinery, New York, NY, USA (2019)
Martella, C., Dobson, M., Van Halteren, A., Van Steen, M.: From proximity sensing to spatio-temporal social graphs. In: Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom 2014), pp. 78–87. IEEE, Piscataway, NJ, USA (2014)
Paulos, E., Goodman, E.: The familiar stranger: anxiety, comfort, and play in public places. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2004), pp. 223–230. Association for Computing Machinery, New York, NY, USA (2004)
Daly, E.M., Haahr, M.: Social network analysis for routing in disconnected delay-tolerant manets. In: Proceedings of the 8th ACM International Symposium on Mobile ad Hoc Networking and Computing, pp. 32–40. ACM, New York, NY, USA (2007)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on Recommender systems, pp. 17–24. Association for Computing Machinery, New York, NY, USA (2007)
Ma, H., Zhou, D., Liu, C., Lyu, M. R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. Association for Computing Machinery, New York, NY, USA (2011)
Veličković, P., Casanova, A., Lió, P., Cucurull, G., Romero, A., Bengio, Y.: Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations (ICLR 2018). OpenReview.net (2018). https://doi.org/10.17863/CAM.48429
Fan, W., et al.: Graph neural networks for social recommendation. In: Proceedings of the World Wide Web Conference (WWW 2019), pp. 417–426. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3308558.3313488
Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., Wang, M.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235–244. ACM Press, New York, NY, USA (2019). https://doi.org/10.1145/3331184.3331214
Chen, Y., Yang, T., Ren, B., Yao, C., Xu, F., Konomi, S.: DREANRec: deep relation enhanced attention networks for social recommendation. In: Proceedings of the 85th National Convention of Information Processing Society of Japan (IPSJ). Information Processing Society of Japan, Tokyo (2023). (in press)
Yang, T., Chen, Y., Ren, B., Yao, C., Xu, F., Konomi, S.: Cost-efficiency analysis in deep relation-enhanced graph attention networks for social recommender systems. In: Proceedings of the 85th National Convention of Information Processing Society of Japan (IPSJ). Information Processing Society of Japan, Tokyo (2023). (in press)
Pu, P., Chen, L., Hu, R.: user-centric evaluation framework for recommender systems. In: Proceedings of the 5th ACM Conference on Recommender Systems (RecSys 2011), pp. 157–164. ACM Press, New York, NY, USA (2011)
van Kleef, G.A., Côté, S.: The social effects of emotions. Ann. Rev. Psychol. 73, 629–658 (2022)
Granovetter, M.S.: The strength of weak ties. Am. J. Soc. 78(6), 1360–1380 (1973)
Acknowledgements
We thank the participants of our user experiment. This work was supported by JSPS KAKENHI Grant Numbers JP17KT0154 and JP20H00622.
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Konomi, S., Hu, X., Chen, Y., Yang, T., Ren, B., Yao, C. (2023). Leveraging Living Trust Networks for Socially-Aware Recommendations. In: Rau, PL.P. (eds) Cross-Cultural Design. HCII 2023. Lecture Notes in Computer Science, vol 14022. Springer, Cham. https://doi.org/10.1007/978-3-031-35936-1_37
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