Tutorial 3C Offline Evaluation for Group Recommender Systems

RecSys 2021 RecSys 2022 Offline Evaluation for Group Recommender Systems by Francesco Barile (Maastricht University, The Netherlands), Amra Delić (University of Sarajevo, Bosnia and Herzegovina), and Ladislav Peška (Charles University, Czech Republic) Group Recommender Systems (GRSs), unlike recommendations for individuals, provide suggestions for groups of people. Clearly, many activities are often experienced by a group rather than an individual (visiting a restaurant, traveling, watching a movie, etc.) hence the requirement for such systems. The topic is gradually receiving more and more attention, with an increased number of papers published at significant venues, which is enabled by the predominance of online social platforms that allow their users to interact in groups, as well as to plan group activities. However, the research area lacks certain ground rules, such as basic evaluation agreements. We believe this is one of the main obstacles to make advances in the research area, and to enable researchers to compare and continue each other’s works. In other words, setting the basic evaluation agreements is a stepping-stone towards reproducible Group Recommenders research. The goal of this tutorial is to tackle this problem, by providing the basic principles of the GRSs offline evaluation approaches. The tutorial is planned for 150 minutes. After introducing the theoretical background, a major part of the allocated time will be dedicated to the interactive participation (group discussions, hands on). In particular, the tutorial cover evaluation with a synthetic data set (a data set that does not contain real information about groups, but the groups are created artificially), i.e., the MovieLens data set, as well as evaluation with a data set containing information about individual as well as group preferences in the tourism domain. The primary target audience of the tutorial will be academic researchers whose research interest involves group recommendation systems and group decision support systems. Furthermore, the tutorial also aims to attract industry researchers whose research initiatives could potentially be extended to group recommendations. Participants are assumed to have a basic knowledge of the Python language and machine learning, and familiarity with recommendation systems and group recommendation systems. In addition, it is recommended to bring a personal laptop for the practical session.