# Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations
## 2 Related Work  
Online education plays a fundamental role in the prevention and conscious participation of patients in the care process. In particular, some online social networks have been emphasized as a useful tool that acts as social support for the elderly, capable of shielding, in some circumstances, from psychological stress and encouraging participation and engagement [4; 5; 6; 7; 8; 9]. Some of these platforms, such as PatientsLikeMe ([[http://www.patientslikeme.com](http://www.patientslikeme.com)]([http://www.patientslikeme.com](http://www.patientslikeme.com))), CureTogether ([[http://www.curetogether.com](http://www.curetogether.com)]([http://www.curetogether.com](http://www.curetogether.com))), or MedHelp ([[http://www.medhelp.org](http://www.medhelp.org)]([http://www.medhelp.org](http://www.medhelp.org))), have provided users self-management of personal activities for their own diseases. For example, in [10], an auto-tracking system called \"Empower\"was designed to allow the self-management of patients and their treating physicians. In this case, specialists can provide recommendations and monitor patient's daily progress. Similarly, HealthNet (HN) [11] allows users to access suggestions on both doctors and health facilities that best fit the patient's clinical profile. The main component of HN is a recommender system that is able to suggest similar profiled patients and indicate health services for a specific condition. Moreover, understanding (or even inducing) the formation of social ties within online health networks, their structure, and functions, as well as the associated mechanisms linking these to health services, can be extremely relevant for several reasons. For example,  
* To reduce information overload [12; 13; 14];
* To provide trust-based communications [15; 16; 17];
* To handle complex relationships among the networks' elements [18];
* To allocate limited resources properly [19; 20]. In this regard, one of the most convincing ideas for promoting the creation of suitable health social spaces has been the application of recommender systems (RS). Unfortunately, these mechanisms have not yet been widely used in the medical context (and in health informatics). Above all, in these fields, they still lack the fulfillment of the above issues simultaneously. To some extent, current solutions are limited to conferring direct benefits either to health professionals or patients, rather than inducing both optimized and reliable information content spaces for targeted allocation of health services. [PERSON] and [PERSON] [21] distinguished two different scenarios. On the one hand, healthcare professionals are the beneficiaries of the recommendations to find, for example, additional information for specific case studies, guidelines, or research articles. On the other hand, the beneficiaries are the patients. In the latter case, RS focuses on delivering high quality, evidence-based, health-related content to end-user patients, for example suggesting clinical examinations [22], lifestyle changes [23; 24], or improving patient safety [25]. Similarly, they are also used to indicate to the patient a better understanding of his/her personal health status, to retrieve semantically-related content, or to suggest web sites concerning specific diseases [26; 27]. To the best of our knowledge, no framework exists that specifies how health-related recommender systems can guide the network creation process and, in particular, can generate networks that comply with the requirements argued previously. It is our opinion that promising new research in this direction can be conducted by applying computational methods to the study of heterogeneous information networks more widely [18; 28; 29; 30; 31; 32]. In this context, graph theory and optimization techniques could provide future recommendation systems with the ability to influence and effectively support user communication in online health spaces.