MediaEval 2015 Context of Experience Task: Recommending Videos Suiting a Watching Situation

MediaEval 2015 Multimedia Benchmark Evaluation

This task tackles the challenge of predicting the multimedia content that users find most fitting to watch in specific viewing situations. Most work on video recommendation focuses on predicting personal preferences. As such, it overlooks cases in which context has a strong impact on preference relatively independently of the personal tastes of specific viewers. Particularly strong influence of context can be expected in unusual, potentially psychologically or physically straining, situations.

In this task, we focus on the case of viewers watching movies on an airplane. Here, independently of personal preferences, viewers share the common goal (which we consider to be a “viewing intent”) of passing the time, and keeping themselves occupied in the small space of an airplane cabin. The objective of the task is to predict which videos allow viewers to achieve this goal, given the context, which includes the limitations of the technology (e.g., screen size), and the environment (e.g., background noise, interruptions, presence of strangers). We choose airplanes since the role of stress, and viewers’ intent to distract themselves is widely acknowledged, e.g., in online descriptions such as [1]. Although this year will limit itself to the airplane scenario, we note that the challenge of Context of Experience is much broader in scope. Other stressful contexts where videos are becoming increasingly important include hospital waiting rooms, and dentists offices, where videos are shown during treatment.

The task will provide participants with a list of movies (including links to descriptions and video trailers), and require them to classify each movie into +goodonairplane/-goodonairplane classes. The ground truth of the task is derived from two sources. First, actual movie lists used by a major airlines, and second user judgments on movies that are collected via a crowdsourcing tool.

Task participants should form their own hypothesis about what is important for users viewing movies on an airplane, and design and approach using appropriate features and a classifier, or decision function.

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