Epidemiology is the study of how often diseases occur in different groups of people and why. Epidemiological information is used to plan and evaluate strategies to prevent illness and as a guide to the management of patients in whom disease has already developed. In the mid-1980s, five major tasks of epidemiology in public health practice were identified: public health surveillance; field investigation, analytic studies; evaluation; and linkages. Public health surveillance is the ongoing, systematic collection, analysis, interpretation, and dissemination of health data to help guide public health decision making and action. Surveillance is equivalent to monitoring the pulse of the community. The purpose of it is to portray the ongoing patterns of disease occurrence and disease potential so that investigation, control, and prevention measures can be applied efficiently and effectively.
Given the importance of movie trailers in the consumer decision process and the large budget all, it is relevant to research how to make this advertising medium more relevant to consumers personally. Bigger, more relevant datasets mean that there is more insight to consumer preferences, which provides ample opportunity to target audiences highly specifically. This thesis proposes a personalized movie trailer based on such consumer preferences. Such a system would help movie studios to target different audiences more effectively, as each consumer will be shown a movie trailer highly relevant to their own personal preferences. For instance, if data shows that user X will watch anything that features actor Y, a trailer that heavily features that actor should be more effective in persuading that user to watch the movie.
Central to linking consumer preferences data to movie trailers are recommendation systems. Recommendation systems (RS) are software tools and techniques that provide suggestions for items that are most likely to be of interest to a user (Ricci, Rokach, & Shapira, 2015). Content-based RSs generate recommendations by combining user feedback on items with the content (i.e., features) associated with them (Lops, De Gemmis, & Semeraro, 2011). Operating by the logic that users will prefer the same features in movies as they do in movie trailers, the following research question is proposed:
“How 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 genr
