Specific movie events can correspond to genres, i.e., a romantic shot in a movie should be classified as belonging to the romance genre, so that it is more likely to be recommended to someone who prefers romantic movies. These movie events are to be manually annotated for each shot as they cannot be automatically detected even using the most modern semantic concept detection methodologies (Kannan et al., 2015). This is because of the highly subjective nature of these movie events, and because “the low-level visual features trained for classification are not highly correlated with the corresponding event” (Kannan et al., 2015).

Visual descriptors
To match the available dataset, visual descriptions from the FC7 layer of the AlexNet convolutional neural network will be used. These represent abstract, top-level features that are discovered in each key frame, and are descriptors of color and texture.

3.3 Training process
The datasets that will be used for the training of the recommendation system are called MMTF-14K (Deldjoo), MovieLens 20M (reference), and UC Irvine Machine Learning Lab’s Movie Data Set, which has data on the cast of over 10,000 movies.

3.4 Summarization
During the summarization process, video segments are ranked based on computed similarity measures between the user profile and the movie features. Personalized movie summarization can be seen as “the process of measuring the similarity score of each video segment for the given user preferences and selecting those top ranked segments that will increase the cumulative similarity score of the summary” (Kannan et al., 2015).
First, the similarity between each shot and the user preferences on actor appearance, genre, and visual descriptors is calculated using cosine similarity measures. Each shot is stored as a vector of its features in a high-dimensional space, after which the angles between the vectors are calculated as the cosine similarity between the vectors. After this, user profiles are created based on their ratings on the sam