  The growth of Social Networks has fueled the habit of people logging their
day-to-day activities, and long First-Person Videos (FPVs) are one of the main
tools in this new habit. Semantic-aware fast-forward methods are able to
decrease the watch time and select meaningful moments, which is key to increase
the chances of these videos being watched. However, these methods can not
handle semantics in terms of personalization. In this work, we present a new
approach to automatically creating personalized fast-forward videos for FPVs.
Our approach explores the availability of text-centric data from the user's
social networks such as status updates to infer her/his topics of interest and
assigns scores to the input frames according to her/his preferences. Extensive
experiments are conducted on three different datasets with simulated and
real-world users as input, achieving an average F1 score of up to 12.8
percentage points higher than the best competitors. We also present a user
study to demonstrate the effectiveness of our method.
