The purpose of this study is to propose a framework for personalized movie trailers based on consumer preferences, and to provide empirical evidence for the effectiveness of such a trailer for audiences. 1.3 Academic relevance Numerous studies in the field of video summarization or video abstraction have been conducted. Video summarization systems select significant segments for users to generate a short version of a lengthy video (Kannan, Ghinea, & Swaminathan, 2015). Existing approaches can be classified into cognitive-level and affective-level approaches, the former extracting low-level features such as color, motion, and composition, and the latter utilizing high-level semantic features such as events and semantic concepts. Most of these approaches focus on analyzing genre-specific movie trailers 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.>