Sample Solution

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.