ilers for patterns to automatically select salient moments in a movie for a movie trailer (Hermes & Schultz, 2006; Smeaton, Lehane, O’Connor, Brady, & Craig, 2006; Smith, Joshi, Huet, Hsu, & Cota, 2017), thus generating a generic summary that is common for all users.
Fewer research has been done with regards to personalizing video summarization. One highly relevant study is by Kannan, Ghinea, and Swaminathan (2015), who propose a novel system that summarizes a movie based on the preferences and interests of the user. Shots and scenes are automatically detected, which are labeled with high-level features. A key difference between this system and the proposed system in this study is the collection of user preferences, which are asked explicitly to the user in order to generate a summary, while in this study user preferences are inferred through existing data.

1.4 Managerial relevance
Most research in this direction points to trailer generation as a labor-intensive process. Hand-selecting shots for movie trailers takes a long time.
The studies mentioned in the section above propose video summarization methods with an interactive component to personalize video summaries to the user. This technology, while relevant to information retrieval research, does not seem applicable or relevant to advertising executives in its usefulness. The proposed summarization system of this thesis will be useful in that existing data on user preferences is leveraged to generate relevant summaries. This could be useful for video-on-demand platforms that have information on the preferences of their users, or for movie studios that wish to advertise tailored movie trailers through other platforms, e.g., Google’s DoubleClick Dymamic Ad Insertion (Marvin, 2016).
2. Literature review