Recent studies are characterized by advanced recommendation techniques and novel aspects, such as social data and temporal features, so the tutorial will also cover these emerging aspects and techniques.

In conclusion, the open issues and research challenges in this area will be presented.

This tutorial will cover these topics in six sections. In detail, the outline of the tutorial is the following.

1. Recommender systems principles
   • Definition and application domains
   • Main classes of systems
2. Group recommendation introduction
   • Definition and application domains
   • Problem statement
3. Tasks and state of the art survey
   • Types of groups. In [2], we highlighted that the type of group handled by a system is one of the characterizing aspects of a group recommender system and we provided a classification of the different types of groups, which was also adopted by Carvalho et al. in their WWW’13 paper [4].
   • Preference acquisition. A group recommender system might acquire the preferences by considering only those expressed by the individual users, or by allowing the groups to express them.
   • Group modeling [9] is the process adopted to combine the individual preferences in a unique model that represents the group.
   • Rating prediction is the most characterizing aspect in all the types of recommender systems, and also plays an important role when working with groups, since the ratings might be predicted for the individual users or specifically for the groups [7].
   • Help the members to achieve consensus. This task is adopted in order to find an agreement on what should be proposed to the group.
   • Explanation of the recommendations, i.e., the task performed by some of the systems to justify why an item has been suggested to the group.
4. Evaluation methods
   • Offline methods, which evaluate a system on existing datasets.
   • User surveys that test the effectiveness of a system by asking users to answer questionnaires.
   • Live systems that work in real-world domains, like the social networks.
5. Emerging aspects and techniques
   • Advanced recommendation techniques applied to group recommendation. The last advances in recommendation techniques, such as generative and stochastic models, have recently been employed in group recommender systems too [8, 12].
   • Temporal aspects in group recommendation. Recently, the temporal factor has been considered in the recommendation process [1, 5].
   • Social group recommender systems. The widespread relevance of social media has recently had an impact also on this research area [6, 8].
   • Group recommendation with automatic detection of groups. There are scenarios in which groups do not exist, but the recommendations have to be proposed to groups because of limitations on the number of recommendation lists that can be produced (i.e., it is not possible to suggest a different list of items to each user), so a clustering of the users specifically designed for recommendation purposes has to be performed [3].
6. Summary
   • Open issues
   • Research challenges

Target Audience