sual aspects of items and less by their semantic features. Deldjoo, Elahi, Quadrana, and Cremonesi (2018) use low-level visual features extracted using the MPEG-7 standard and a deep neural network (DNN). The MPEG-7 standard extracts visual descriptors of images as color descriptors and texture descriptors. Alternatively, the authors used the activation values of inner neurons of the GoogLeNet DNN as visual features for each key frame. Whereas MPEG-7 features capture stylistic descriptors (i.e., color and texture), DNN features capture semantic content (e.g, objects, people, etc.). In this study, MPEG-7 features generated more accurate recommendations than semantic features (DNN). This could be due to the fact that while a DNN recognizes relevant semantic features (such as actors), it also recognizes non-relevant semantic features, which can create noise in the dataset.

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