# Multidimensional Group Recommendations in the Health Domain
###### Abstract  
Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients' welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents. recommendations; group recommendations; semantic similarity; group aggregation +
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0000-0001-454-1000-0000  
0000-0001-454-1000]R.M. would be the most constructive for each individual patient. However, the amount of time that a health provider can dedicate to each patient has greatly declined. Consequently, they have an even more difficult task in guiding their patients. Aside from the guidelines provided by health providers, another support for the patients is their social circle. The use of group dynamics-based principles of behavior change have been shown to be highly effective in enhancing social support, e.g., through promoting group cohesion in physical activity [4] and in reducing smoking relapse [5]. Especially for cancer, the latest studies [6] suggest that group therapy improves the well-being of cancer patients because of enhanced discussion and social support. In these counseling meetings, the patient is directed towards the most informative and reliable sources on the Web. However, the effort of locating pertinent information for a group of participants is far greater than identifying information for just one patient. This motivates us to concentrate our efforts on recommending to a group of patients relevant and interesting health documents that were selected by health professionals. We utilize the collaborative filtering (CF) recommendation model for this task. Our motivation for this work is to offer to a caregiver that is in charge for a group of patients a recommendation list that consists of health documents that are relevant to the group members. The relevance of the recommended documents is calculated based on the patients' current health profiles. In addition, we would like to identify information that is equally fair to all members, meaning no member in the group is unsatisfied. We incorporate fairness during the aggregation phase of our recommendation model. To produce group recommendations, one must first produce recommendation lists for each group member and then aggregate those into one list that is then reported back to the group. There are many methods to ensure that the aggregation is done fairly [7; 8]. Intuitively, to achieve fair recommendation we consider that all the group members are equal to each other. Therefore, the group score for an item \(i\) is the average score across all the group members' preference scores for that item. Such an approach, however, can easily ignore the opinion of the minority. For example, in a group that consists of three patients, if for all items two of the members have high relevance score, but the third is low, then the opinion of the third member is overshadowed by the other two. To counter such a drawback, we propose a new aggregation method that is done in phases. In each phase, we select one item to include in the group recommendation list.