Continuing improvements to the welfare of animals used in science have occurred over the past few decades partly because of the explicit adoption of a set of principles to guide the ethical evaluation of animal use. This is the “Three Rs” tenet – Replacement, Reduction and Refinement. The tenet is grounded in the premise that animals should be used only if a scientist`s best efforts to find a nonaminal alternative have failed. Use of the Three Rs tenet assists in improving the welfare of animals used in science in several ways: it addresses a range of concerns about scientific animal use; it places a focus on individual animals; it adapts and responds to new information; it balances the needs of science and the needs of the animals; and it unites disparate groups with an interest in the welfare of animals used in science.
tive 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 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 based on high-level features.
2.3 Features
An area of heavy debate within video summarization and recommendation literature is the tradeoff between low-level features and high-level features, the former expressing semantic properties of media content that are obtained from meta-information (e.g., plot, genre, director, actors), and the latter being extracted directly from the media file itself, typically representing design aspects of a movie (such as lighting, colors, and motion). This tradeoff naturally forms a semantic gap problem that has been discussed heavily in the literature.
Much of the video summarization and recommendation literature is guided by the assumption that user preferences are influenced by high-level features to a greater extent than low-level features. For instance,
2.3.1 Low level features
Recent literature on RSs suggest that consumer preferences when c
