# Multidimensional Group Recommendations in the Health Domain
## 2 Related Work  
Typically, recommendation approaches [14] are distinguished between content-based, which recommends items similar to those the user previously preferred (see, e.g., [15]), and collaborative filtering, which recommend items that users with similar preferences like (see, e.g., [16]). Nowadays, recommendations have more broad applications [17], beyond products, like links (friends) recommendations [18], query recommendations [19], open source software recommendations [20], diverse venue recommendations [21], sequential recommendations [22; 23], or even recommendations for evolution measures [24; 25]. Although traditional research on recommender systems has almost exclusively focused on providing recommendations to single users, there exist many cases where the system needs to suggest items to groups of users [26; 27]. As an example consider a group of friends deciding to dine at a restaurant. Typically, for producing group recommendations, we first compute recommendations for each group member separately, and then employ an aggregation strategy across them to compile the group recommendations (see, e.g., [28; 29]). Various aggregation strategies can be applied to find a consensus between users for particular items, by minimizing, for instance, the disagreements between the group members. More recently, the authors of [30] analyze the problem of recommending sets of items to groups incorporating factors, like user impact, viability, and fairness. Recommendations in the Health Domain  
Nowadays, patients turn towards the Web to inform themselves about their diseases and their possible treatment. This suffers from two main problems. First, the information found on the Web is not always accurate, and second, it is very diverse. To face these problems a personalized recommender would allow the users to have a seamless, secure, and consistent bidirectional linking of clinical research and clinical care systems, and thus empowering the patients to extract the relevant data out of the overwhelming large amounts of heterogeneous data and treatment information. The authors of [31] portray the requirements that a Health Recommender System (HRS) needs to fulfill, whereas the authors of [32] analyze common pitfalls of such systems. For a recent survey for recommender systems for health promotion, the interested reader is forwarded to [33]. In this line of work, there have been already developed many recommendation systems focusing on citizen's wellbeing. For example, the authors of [34; 35] propose web-based recommender systems that provides individualized nutritional recommendations according to the user's health profile defined, by following the main guidelines furnished by a medical specialist, whereas the authors of [36] suggest messages relevant to the user to support the smoking cessation process. The work in [37] is a recommender system proposing physical activities using only user's history and employing machine learning, whereas for chronic conditions, other works focus on integrating recommender systems with electronic health records [38; 39], proposing the best course of treatment. Other approaches adapt past recommendations to the current state of the user for Diabetes patients [40] or propose context-aware recommendation methods [41] to establish personalized healthcare services. However, all these works use techniques that are principally found in pure group recommendations systems for composing the group recommendation list. However, we have tailored our recommendations for the health domain, exploiting the semantically annotated PHR profile of the users. This directly allows us to endorse documents that are relevant to a user not only on the level of appreciation (meaning the ratings that each item has gained), but also on the level of his personal health profile (we recommend items relevant to him because of related health artifacts).