In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems

This paper deals in depth with some of the emotions that play a role in a group recommender system, which recommends sequences of items to a group of users. First, it describes algorithms to model and predict the satisfaction experienced by individuals. Satisfaction is treated as an affective state. In particular, we model the decay of emotion over time and assimilation effects, where the affective state produced by previous items influences the impact on satisfaction of the next item. We compare the algorithms with each other, and investigate the effect of parameter values by comparing the algorithms' predictions with the results of an earlier empirical study. We discuss the difficulty of evaluating affective models, and present an experiment in a learning domain to show how some empirical evaluation can be done. Secondly, this paper proposes modifications to the algorithms to deal with the effect on an individual's satisfaction of that of others in the group. In particular, we model emotional contagion and conformity, and consider the impact of different relationship types. Thirdly, this paper explores the issue of privacy (feeling safe, not accidentally disclosing private tastes to others in the group) which is related to the emotion of embarrassment. It investigates the effect on privacy of different group aggregation strategies and proposes to add a virtual member to the group to further improve privacy.