In this paper, we present the dataset Right Inflight developed to support the exploration of the match between video content and the situation in which that content is watched. Specifically, we look at videos that are suitable to be watched on an airplane, where the main assumption is that that viewers watch movies with the intent of relaxing themselves and letting time pass quickly, despite the inconvenience and discomfort of flight. The aim of the dataset is to support the development of recommender systems, as well as computer vision and multimedia retrieval algorithms capable of automatically predicting which videos are suitable for inflight consumption. Our ultimate goal is to promote a deeper understanding of how people experience video content, and of how technology can support people in finding or selecting video content that supports them in regulating their internal states in certain situations. Right Inflight consists of 318 human-annotated movies, for which we provide links to trailers, a set of pre-computed low-level visual, audio and text features as well as user ratings. The annotation was performed by crowdsourcing workers, who were asked to judge the appropriateness of movies for inflight consumption.@en

In this paper we present an overview of the Context of ExperienceTask: recommending videos suiting a watching situationwhich is part of the MediaEval 2016 Benchmark. Theaim of the task is to explore multimedia content that iswatched under a certain situation. The scope of the thisyears task lies on movies watched during a flight. We hypothesizethat users will have different preferences for moviesthat are watched during a flight compared to when a movieis watched at home or the cinema. This is most probablyinfluenced by the context and the devices used to watch. Inthe case of being on a flight, the context is clearly differentto normal situation (noise, compact, bad air) and also thedevices differ (small screens, bad audio quality). The maingoal of the task is to estimate if a person would like to watcha certain movie on the airplane or not. As dataset we providea large collection of movies, collected from an airline,including pre-extracted visual, text and audio features.@en