bernitz (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 of those movies, an item profile is to be built, which is a description of the movie based on a number of pre-determined features. From these movies, a user profile will be inferred. For instance, because the user likes movies with Brad Pitt, it will be inferred that the user will prefer shots with Brad Pitt in a movie trailer. Because the user likes horror movies, it will be inferred that the user likes shots that are stylistically similar to horror movies.
Informed by the literature discussed above, the following features will be used to create item profiles for the content-based recommendation system:
1. Actor appearance. As one of the most important influencers on film quality expectations, actor appearance should be taken into account as a feature to guide scene recommendation. Actor appearances can be extracted from the IMDB dataset. The logic that this follows from is that if a user likes multiple movies that feature the same actor, this actor should have a high degree of importance in building a user profile.
2. Genre. As discussed above, genre is an important influence on film content expectations and a widely-used feature for segmenting audiences. Genre features are included in the MMTF-14K dataset.
3. Visual descriptors. Low-level visual features have been shown to be very representative of the user’s feelings, according to the theory of Applied Media Aesthetics (Deldjoo et al., 2018). The MMTF-14K datasets has aesthetic descriptors and object and scene descriptors extracted from the FC7 layer of the AlexNet convolutional neural network.