Write a five-paragraph essay comparing the character Beowulf to King David in the Bible. Find three things that both characters have in common.
Sample Solution
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 userâs 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 entails that a large matrix of ratings can be expressed as a product of smaller matrices in order to save storage space (Serrano, 2018). In extension of their previous work on content-based recommendation, Deldjoo, Elahi, and Cremonesi (2016) propose a recommendation system based on Factorization Machines (a combination of Support Vector Machines and MF) and low-level stylistic features. RSs based on CF often have to be supplemented with side information to maintain a rich set of high-level descriptive attributes about movies for newly released movies, which is often human-generated and prone to biases and errors. Analyzing low-level stylistic features to make recommendations can solve this and can address the problem of a new item being added with no high-level attributes. The results show that recommendations based on low-level visual features achieve almost 10 times better accuracy in comparison to those that are base>
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 userâs 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 entails that a large matrix of ratings can be expressed as a product of smaller matrices in order to save storage space (Serrano, 2018). In extension of their previous work on content-based recommendation, Deldjoo, Elahi, and Cremonesi (2016) propose a recommendation system based on Factorization Machines (a combination of Support Vector Machines and MF) and low-level stylistic features. RSs based on CF often have to be supplemented with side information to maintain a rich set of high-level descriptive attributes about movies for newly released movies, which is often human-generated and prone to biases and errors. Analyzing low-level stylistic features to make recommendations can solve this and can address the problem of a new item being added with no high-level attributes. The results show that recommendations based on low-level visual features achieve almost 10 times better accuracy in comparison to those that are base>
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