It is a belief that if one country fell to communism, it was likely that the neighboring one would also fall. This had happened in Eastern Europe after 1945. China had become communist in 1949 and communists were in control of North Vietnam. The reason why the USA participated in the Vietnam War was because it was afraid that communism would spread to Southern Vietnam and then the rest of Asia. It decided to send money, supplies and military advisers to help the South Vietnamese Government. Also, the North Vietnamese attacked the US navy in the Gulf of Tonkin. This incident gave the USA the excuse it needed to escalate the war. As the Vietnam War escalated, America`s leaders and soldiers found themselves in a quagmire with no victory in sight. Eventually the war weakened the American economy, divided the people, and eroded the nation`s morale.
ow can recommendation systems guide the generation of personalized movie trailers, and how effective is personalization in movie trailers?”
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.
