related literature that has been written on the topic of personalized video summarization and detail the supporting literature that will form the basis for the conceptual framework of this thesis.

2.1 Personalized video summarization
Numerous studies researching personalized video summarization or video abstraction have been conducted. 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, for which high-level features are semi-automatically annotated. One 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 to generate a summary, while in this study user preferences are inferred implicitly.

Most of these approaches explicitly obtain users’ preferences for shot level personalized video summarization.

2.1.1 The Semantic Gap
A problem often encountered in video summarization and movie recommendation is the semantic gap (references). The semantic gap is the gap between the high-level concepts that users expect when searching for interesting multimedia content (e.g., genre, plot, actors) and the low-level features that it is possible to automatically extract from the same content (e.g., brightness, contrast, etc.). This gap represents two research directions, the first being mostly explored by researchers with a background in film theory and the latter being focused on mainly by computer scientists (Hermes & Schultz, 2006).

2.2 Recommendation systems
For the purposes of this research, recommendation system literature will be adapted to select scenes for a