Home entertainment systems feature in a variety of usage scenarios with one
or more simultaneous users, for whom the complexity of choosing media to
consume has increased rapidly over the last decade. Users' decision processes
are complex and highly influenced by contextual settings, but data supporting
the development and evaluation of context-aware recommender systems are scarce.
In this paper we present a dataset of self-reported TV consumption enriched
with contextual information of viewing situations. We show how choice of genre
associates with, among others, the number of present users and users' attention
levels. Furthermore, we evaluate the performance of predicting chosen genres
given different configurations of contextual information, and compare the
results to contextless predictions. The results suggest that including
contextual features in the prediction cause notable improvements, and both
temporal and social context show significant contributions
