This paper describes three algorithms to model and predict the satisfaction experienced by individuals using a group recommender system which recommends sequences of items. Satisfaction is treated as an affective state. In particular, we model the wearing off 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 show a way in which affective state can be used in recommender systems, which is useful for recommendations not only to groups but also to individuals.