To Binge or not To Binge: viewers’ moods and behaviors during the consumption of subscribed video streaming

The popularity of internet-distributed TV entertainment services, such as Netflix, has transformed TV consumption behavior. Currently, the level of control viewers have over their TV experiences, along with the release of com plete seasons at once, are some of the factors that stimulate the so-called binge watching phenomenon (the consumption of several episodes of a program in a single sitting). Most of binge-watching studies have focused on viewers’ habits and health effects. This paper presents a study that relates to viewers’ behaviors and moods. It was carried out with 13 young participants at their home, watching online content, collecting physiological, inertial, and self-reported data. We iden tify and compare binge-watching with non-binge-watching behaviors. Our results suggest that while viewers recur to online serial entertainment in pursuit of lei sure related needs, such as relaxation, relief from boredom and escapism, the act of binge-watching tends to make them feel rather unsatisfied with no change in Arousal. Nevertheless, in binge-watching the Positive Affect increases while the Negative decreases. Moreover, watching a single episode only, tends to result in increased arousal and but not necessarily in increased satisfaction. This prelimi nary finding can be the starting point of fruitful future investigations on unpack ing further motives and nuances from this outcome.info:eu-repo/semantics/publishedVersio

The Importance of Context When Recommending TV Content: Dataset and Algorithms

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