# Multidimensional Group Recommendations in the Health Domain
###### Abstract
At the beginning of each phase, if there is a member that is not as satisfied as the rest of the group, we select an item based on two criteria. First, it has to have high relevance for that user, and second, it is the best one available for the rest of the group. As we have already mentioned, to generate these recommendations we use the collaborative filtering method. The basic principle of CF is to find similarities between users. Given a target user, we locate other similar users, who are often called peers or neighbors, and utilizing the ratings that peers have given, we estimate relevance scores for the items that the target user has not yet rated. In our work, to calculate similarities between two patients, we consider their health profiles. The information that is included in these health profiles is the following; the ratings that the patient has given to health documents and the health problems they have. Additionally, each patient has been questioned about their education level and their health literacy--meaning to what degree they are able to understand basic information and services related to the health domain. Furthermore, they are periodically questioned about their psycho-emotional status. Specifically, they regularly fill in a questionnaire about their anxiety levels and cognitive closure, meaning the patient's need when faced with a decision, to have an answer instead of continued uncertainty. We propose a new similarity measure that combines all these different sources of information to different degrees to find similarities between patients across all dimensions. In the past, we have proposed a semantic similarity function that takes into account the patients medical profiles, showing its superiority over a traditional measure [9; 10] in group recommendations in the health domain. In addition, we have focused on the notion of fairness [11], devising an aggregation method for ensuring that if the group recommendation list provides a high relevant document for a patient, then that patient may be tolerant of the existence of documents that are not relevant to him/herHowever, although usually health professionals target closely related health problems, the education level, health literacy level, and psycho-emotional status of the group are of high importance, as the content that the health professional should recommend, should be based on the aforementioned axes. To this direction we further extend the dimensions considered for finding similar users and we introduce a new aggregation method called _AccScores_, outperforming existing ones. More specifically, the contributions of our work are the following. 1. We demonstrate a multidimensional group recommendation model in the health domain, using collaborative filtering. 2. We propose a novel semantic similarity function that takes into account, in addition to the patients medical problems, the education, the health literacy and the psycho-emotional status of the patients, showing its superiority over a traditional measure. 3. We introduce a new aggregation method accumulating preference scores, called _AccScores_, showing that it dominates other aggregation methods and is able to produce fair recommendations to small groups of patients. 4. We experimentally show the value of our approach, introducing the first synthetic dataset with such information for benchmarking works in the area. This paper significantly extends our previous work in [11], by introducing two new similarity measures and a way to combine the different similarities functions into one. Furthermore, we introduce a new aggregation method and we present the relevant experiments. To our knowledge, this is the first work in group recommendations in the health domain considering multiple dimensions for increasing the quality of the proposed recommendations. The requirements for generating such a tool originally came from the iManageCancer [12] and the BOUNCE [13] H2020 EU research projects. The rest of this paper is structured as follows. Section 2 presents related work.