Sample Solution

Some studies have attempted to bridge the semantic gap by using both high-level and low-level features. For instance, Hermes and Schultz (2006) used face detection, cut detection, motion analysis, and text detection to be extracted automatically, and background information to be extracted from the Internet Movie Database (IMDb). Xu and Zhang (2013) use motion analysis, face recognition, sound volume detection, speech and music detection, and low-level features of brightness, contrast, and shot length.

2.3.2 Importance of semantic features
As this research is conducted within the context of marketing, attention has to be paid to which movie trailer features are most indicative of consumer’s willingness to see the movie. In a qualitative exploratory study on New Zealand film audiences by Finsterwalder, Kuppelwieser, and De Villiers (2012), it was found that actors are the greatest influencers on film quality expectations, and genre the most important influence on film content expectations. Moreover, consumers enjoying the music in a trailer may find the potential film increasingly interesting. Similarly, Karray and Debernitz (2017) found that the appeal of the plot, the number of scene cuts and the inclusion of violent, sexual, or humorous scenes influence the movie’s abnormal returns.

3. Methodology
The objective of this research is to use content-based recommendation to recommend shots to a user that contain similar characteristics to movies that user previously rated highly. This entails finding a set of movies the user likes using the MovieLens 20-M dataset. For each