Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations
Source abstract
Abstract
Conscious and functional use of online social spaces can support the elderly with mind cognitive impairment (MCI) in their daily routine, not only for systematic monitoring, but to achieve effective targeted engagement. In this sense, although social involvement can be obtained when elder’s experiences, interests, and goals are shared and accepted by the community, an important subsistence for aging depends on the compelling information, users’ co-operation, and resource reliability. Unfortunately, applications aimed at optimizing the information content and the reliability of online users are still missing. Within the SystEm of Nudge theory-based ICT applications for OldeR citizens (SENIOR) project, an advanced social platform will be created in which the elderly with MCI will be involved in “optimized” social communities, where suggestions for general well-being will be recognized as useful by users and shared by care providers. We report the results of our study addressing this issue from a theoretical perspective: we propose a computational problem and a heuristic solution where “expert users” can engage and support the elderly by suggesting available services and facilities for their conditions. The numerical experiments on synthetic data are of interest when considering large communities, which is the most natural situation for online social spaces.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Technology Use by Older Adults · Human Mobility and Location-Based Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, 20126 Milano, Italy
Department of Computer Science, Università degli Studi di Milano, 20133 Milano, Italy
Department of Psychology, Catholic University of Milan, 20123 Milano, Italy
Istituto Auxologico Italiano IRCCS, Psychology Research Laboratory, San Giuseppe Hospital, 28824 Verbania, Italy
Funding
Fondazione Cariplo
References (49)
Show source-provided references
1. Mild cognitive impairment: Clinical characterization and outcome10.1001/archneur.56.3.303 ↗
2. Arno, A., and Thomas, S. (2016). The efficacy of nudge theory strategies in influencing adult dietary behaviour: A systematic review and meta-analysis. BMC Public Health, 16.10.1186/s12889-016-3272-x ↗
3. Richard, H., and Thaler, C.R.S. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness, Yale University Press.
4. Resources and repertoires: Elderly online practices10.1177/0267323118810852 ↗
5. The influence of social support on loneliness and depression among older elderly people in China: Coping styles as mediators10.1002/jcop.22185 ↗
6. Haritou, M., Anastasiou, A., Kouris, I., Villalonga, S.G., Gancedo, I.O., and Koutsouris, D. (2013, January 29–31). Go-myLife: A context-aware social networking platform adapted to the needs of elderly users. Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments, Island of Rhodes, Greece.10.1145/2504335.2504343 ↗
7. Harley, D., Howland, K., Harris, E., and Redlich, C. (2014, January 9–12). Online communities for older users: What can we learn from local community interactions to create social sites that work for older people. Proceedings of the 28th International BCS Human Computer Interaction Conference (HCI 2014), Southport, UK.10.14236/ewic/HCI2014.8 ↗
8. Pensas, H., Kivimäki, T., Vainio, A.M., Konakas, S., Costicoglou, S., Kölndorfer, P., Summanen, K., Moisio, H., and Vanhala, J. (2013, January 1–4). Building a client-server social network application for elders and safety net. Proceedings of the International Conference on Making Sense of Converging Media, Tampere, Finland.10.1145/2523429.2523435 ↗
9. Dangon, J.M., and Mendoza, A. (2013, January 22–24). A Conceptual Framework to Evaluate Usability in Mobile Aged Care Applications: A health care initiative. Proceedings of the International Conference on Information Resources Management: Managing IT in a Consumerized IT World, Natal, Brazil.
10. Mantwill, S., Fiordelli, M., Ludolph, R., and Schulz, P.J. (2015). EMPOWER-support of patient empowerment by an intelligent self-management pathway for patients: Study protocol. BMC Med. Inform. Decis. Mak., 15.10.1186/s12911-015-0142-x ↗
11. Power to the patients: The HealthNetsocial network10.1016/j.is.2017.07.005 ↗
12. Information and communication technology overload and social networking service fatigue: A stress perspective10.1016/j.chb.2015.08.011 ↗
13. Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services10.1016/j.im.2016.03.006 ↗
14. Bawden, D., and Robinson, L. (2020). Information Overload: An Overview, Clarendon Press.10.1093/acrefore/9780190228637.013.1360 ↗
15. A review on trust propagation and opinion dynamics in social networks and group decision making frameworks10.1016/j.ins.2018.11.037 ↗
16. Golbeck, J. (2006, January 16–19). Generating predictive movie recommendations from trust in social networks. Proceedings of the International Conference on Trust Management, Pisa, Italy.10.21236/ADA447900 ↗
17. MTPKM: Multipart trust based public key management technique to reduce security vulnerability in mobile ad-hoc networks10.1007/s11277-019-06588-4 ↗
18. Feature-rich networks: Going beyond complex network topologies10.1007/s41109-019-0111-x ↗
19. Social networks and collateral health effects10.1136/bmj.329.7459.184 ↗
20. Network interventions10.1126/science.1217330 ↗
21. Health recommender systems: Concepts, requirements, technical basics and challenges10.3390/ijerph110302580 ↗
22. Pattaraintakorn, P., Zaverucha, G.M., and Cercone, N. (2007, January 14–16). Web based health recommender system using rough sets, survival analysis and rule-based expert systems. Proceedings of the International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, Toronto, ON, Canada.10.1007/978-3-540-72530-5_59 ↗
23. Liang, Y. (2019, January 16–20). Recommender system for developing new preferences and goals. Proceedings of the 13th ACM Conference on Recommender Systems, Copenhagen, Denmark.10.1145/3298689.3347054 ↗
24. Vairale, V.S., and Shukla, S. (2019). Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic Review. Data Science and Big Data Analytics, Springer.10.1007/978-981-10-7641-1_29 ↗
25. Roitman, H., Messika, Y., Tsimerman, Y., and Maman, Y. (2010, January 11–12). Increasing patient safety using explanation-driven personalized content recommendation. Proceedings of the 1st ACM International Health Informatics Symposium, Arlington, VA, USA.10.1145/1882992.1883057 ↗
26. Wiesner, M., and Pfeifer, D. (2010, January 11–12). Adapting recommender systems to the requirements of personal health record systems. Proceedings of the 1st ACM International Health Informatics Symposium, Arlington, VA, USA.10.1145/1882992.1883053 ↗
27. Morrell, T.G., and Kerschberg, L. (2012, January 1–5). Personal health explorer: A semantic health recommendation system. Proceedings of the 2012 IEEE 28th International Conference on Data Engineering Workshops, Arlington, VA, USA.10.1109/ICDEW.2012.64 ↗
28. SemRec: A personalized semantic recommendation method based on weighted heterogeneous information networks10.1007/s11280-018-0553-6 ↗
29. HEEL: Exploratory entity linking for heterogeneous information networks10.1007/s10115-019-01354-1 ↗
30. Hu, J., Cheng, R., Chang, K.C.C., Sankar, A., Fang, Y., and Lam, B.Y. (2019, January 8–12). Discovering maximal motif cliques in large heterogeneous information networks. Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China.10.1109/ICDE.2019.00072 ↗
31. A survey of heterogeneous information network analysis10.1109/TKDE.2016.2598561 ↗
32. Mining heterogeneous information networks: Principles and methodologies
33. Top-k overlapping densest subgraphs10.1007/s10618-016-0464-z ↗
34. Dondi, R., and Hermelin, D. (2020). Computing the k Densest Subgraphs of a Graph. arXiv.
35. Partitioning networks into cliques: A randomized heuristic approach
36. Dondi, R., Mauri, G., Sikora, F., and Italo, Z. (2019). Covering a graph with clubs. J. Graph Algorithms Appl., 23.10.7155/jgaa.00491 ↗
37. Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C. (2009). Introduction to Algorithms, MIT Press. [3rd ed.].
38. Orthology correction for gene tree reconstruction: Theoretical and experimental results10.1016/j.procs.2017.05.047 ↗
39. An exact algorithm for the maximum k-club problem in an undirected graph10.1016/S0377-2217(01)00133-3 ↗
40. On the tractability of finding disjoint clubs in a network10.1016/j.tcs.2019.03.045 ↗
41. Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24–27). Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, New York, NY, USA.10.1145/2623330.2623732 ↗
42. Comparing incomplete sequences via longest common subsequence10.1016/j.tcs.2019.09.022 ↗
43. Goldberg, D.E. (2006). Genetic Algorithms, Pearson Education.
44. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art10.1016/j.asoc.2015.04.061 ↗
45. GA: A Package for Genetic Algorithms in R10.18637/jss.v053.i04 ↗
46. Bollobas, B. (2001). Random Graphs, Cambridge University Press.
47. Asahiro, Y., Miyano, E., and Samizo, K. (2010, January 19–23). Approximating Maximum Diameter-Bounded Subgraphs. Proceedings of the LATIN 2010: Theoretical Informatics, 9th Latin American Symposium, Oaxaca, Mexico.10.1007/978-3-642-12200-2_53 ↗
48. Ma, H., Zhou, D., Liu, C., Lyu, M.R., and King, I. (2011, January 9–12). Recommender systems with social regularization. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China.10.1145/1935826.1935877 ↗
49. Collaborative filtering with social regularization for TV program recommendation10.1016/j.knosys.2013.09.018 ↗