We can work on Aristotle’s Six Elements of Drama

What is the title of the performance you viewed? (1 point)
Which of Aristotle’s Six Elements of Drama did you find most important when this performance? Why was it important? Give a specific example from the performance. (3 points)
Which design area had the most impact on your experience of this performance? Why? Give a specific example from the performance. (3 points)
What do you think the director’s concept was for this performance? How did it add or detract from the storytelling? Please give a specific example from the performance. (3 points)

Sample Solution

A problem often encountered in video summarization and movie recommendation is the semantic gap (references). The semantic gap is the gap between the high-level concepts that users expect when searching for interesting multimedia content (e.g., genre, plot, actors) and the low-level features that it is possible to automatically extract from the same content (e.g., brightness, contrast, etc.). This gap represents two research directions, the first being mostly explored by researchers with a background in film theory and the latter being focused on mainly by computer scientists (Hermes & Schultz, 2006). 2.2 Recommendation systems For the purposes of this research, recommendation system literature will be adapted to select scenes for a personalized trailer. Two main avenues can be found in recommendation system research: content-based recommendation and collaborative filtering. Content-based RSs create a profile of a user’s preferences by combining feedback on items with the content (i.e., features) associated with them. This feedback, or ratings, can be gathered explicitly (by asking) or implicitly (by analyzing activity). Recommendations are generated by matching the user profile against the features of all items, computing similarity measures with the unknown item (Lops et al., 2011). An example of such an approach is proposed by Deldjoo et al. (2016), wherein a content-based algorithm based on cosine similarity between items was used on a small dataset of 160 movies was used to provide recommendation based on low-level visual features. Recommender systems typically use two types of item features, namely high-level features and low-level features, the former expressing semantic properties of media content that are obtained from meta-information from databases, lexicons, reviews, or news articles, and the latter being extracted directly from the media file itself, typically representing design>

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