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.

3.1 Movie processing
3.1.1 Video segmentation
The goal is to segment a movie into shots and to select a representative key frame from each shot. IBM’s Multimedia Analysis and Retrieval System (IMARS) (Natsev, Smith, Tešié, Xie, & Yan, 2008) will be used for shot boundary detection. Since a key frame can represent a shot, the middle frame from each shot will be extracted as key frame for visual analysis.

3.1.2 Feature Extraction
Given the segmented clips, features are extracted in terms of actor appearance, genre, and visual descriptors.

Actor appearance
Actors are key to a consumer’s expectations of the movie. A good personalized trailer would feature those actors that are most relevant to a user’s interests. To recognize these actors, the easiest way is to use face recognition. A face recognition system using Eigenfaces (Turk & Pentland, 1991) will be imp