- Choose two of the following assets/attractions, look them up online and use the AR/TI Scorecard to assign them a grade and number, then explain your scores.
Lincoln Center – New York City
National Corvette Museum – Bowling Green, Kentucky
Hadrian’s Wall – Borderlands of England and Scotland
Chichén Itzá – Yucatan State, Mexico
The French Quarter – New Orleans, Louisiana
Monticello – Thomas Jefferson’s home – Charlottesville, Virginia
.1.1 The Semantic Gap 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 aspects of a movie (such as lighting, colors, and motion). The researchers found that recommendations based on low-level stylistic visual features are better than recommendations based on high level semantic features, and that low-level features extracted from trailers can be used as an alternative to features extracted from full-length movies in building content-based recommender systems. The collaborative filtering (CF) approach produces recommendations of items based on patterns of ratings (Koren & Bell, 2015). Using a neighborhood approach, the objective is to find a set of other users whose ratings are similar to the user’s ratings, in order to infer that preferences of the neighborhood are also applicable to the user. There are two approaches to CF: user-user and item-item, the latter of which is considered to perform better (Lescovec et al., 2016). One approach to CF that has been popularized by the recommendation system of Netflix is matrix factorization (MF), which en>GET ANSWER