# Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations
## 7 Conclusions  
The challenge set by the SENIOR project is to involve the elderly, with mind cognitive impairment, in target communities where the shared information and the available resources can be exploited as best as possible by users and caregivers. The SENIOR social engine will be designed as a specific recommender system able to leverage on-line information to influence elderly participation and to promote resources for their well-being. As recognized above, to the best of our knowledge, no framework exists that specifies how health-related recommender systems can promote this \"network creation process\". In this regard, the expected value of the proposed ideas is to promote the creation of online \"health spaces\" by enhancing trust-based communication between patients and \"experts\" for suitable resource allocation. In this model, the \"experts\" not only play an essential role for patients in the real world, but represent mediation nodes for suggesting \"items\" (i.e., resources) accurately. The recommender system should, therefore, influence the development of the social network with appropriate suggestions. In conclusion, we can characterize the contributions of this article as follows. * We formulated a computational optimization problem for the future suggestions of the SENIOR recommender system. * Algorithmic solutions were proposed as well, based on evolutionary heuristics, both for centralized and parallel processing environments. Further extension of our research will be addressed as follows. * As reported in recent studies, using additional user and item relationships could improve the recommendation quality [48; 49]. In this perspective, the optimization objective formulated in Equation (1) will be further constrained by taking into account relationships between target users. These relationships could be interpretable as either \"friendships between target users\" or \"targets sharing similar experiences\". The feasible solutions (i.e., two-clubs) optimized with such a type of connection would allow the future recommendation system to induce even more compelling communication among users, perhaps looking for similar reliable resources. Furthermore, edges among \"experts \" (within the identified two-clubs) could be used as an endorsement, thus allowing target users to evaluate new different experiences or the consistency of the information of other expert users. Such links could also induce more effective communication between experts to facilitate final patient support. Similarly, a constrained optimization accounting for \"resource to resource\" relationships, within the identified community, could offer the user equivalent available resources. * In Section 4.2, we presented a general fitness formulation for two different evaluations of a free parameter (\(\alpha\)). A more context-based formulation able to manage real needs will certainly provide more effective results in this sense. As an example, we can consider the first reported case (Number 1), where \(\alpha\) is related to the health service and vertex-type distributions. In this situation, a linear combination of entropies is used to optimize (and balance) an induced two-club community. Direct comparisons (e.g., using cross-entropy evaluations) of user requirement distribution vs. service distribution could be similarly evaluated for future numerical experiments. * Finally, we assumed so far that each provider could supply one service only. In fact, this was \"coded\" by the labeling function \(h\) discussed in Section 3.2. It is straightforward to extend this labeling to multiple services for a more realistic implementation. Conceptualization, I.Z., A.T., S.M., and D. M.; formal analysis, I.Z. ; funding acquisition, I.Z. and G.C. ; methodology, G.C. ; project administration, G.C. ; resources, G.M. and G.P. ; software, I.Z. ; writing, original draft, S.M. ; writing, review and editing, I.Z., A.T., S.M., D.M., G.M., G.P., and G.C. All authors read and agreed to the published version of the manuscript.