Movie recommendation based on stylistic visual features.

When a new movie is added to the catalogue of a recommendation-empowered movie streaming platform, the system exploits various types of data (e.g., clicks, views, and ratings) in order to generate personalized recommendations for the users. However, in the absence of sufficient data, undesired situations can arise where the system may fail to include the new movie in the recommendation list. This is known as the Cold Start problem. A solution can be using content features attributed to the movies (e.g., tags, genre, and description). However, such features require expensive editorial efforts and it is not necessarily available in good quantity or quality. This thesis investigates the viability of using novel stylistic visual features as metadata to incorporate in the movie recommendation process. The visual features represent the stylistic properties of the movies and can have a wide range of forms, e.g., color palette, contrast, and brightness. The stylistic visual features can be automatically extracted, and hence, do not require any (manual) human annotation. Accordingly, the thesis proposes a novel technique for generating recommendation based on such visual features and describes the technical details for different stages of the process. The technique has been evaluated in both offline and online experiments and different scenarios, i.e., cold start and warm start. The online experiment has been conducted in collaboration with TV 2, one of Noways largest digital streaming platforms adopting an A/B testing methodology. The proposed technique includes utilizing the extracted visual features when used individually (in a similarity based recommendation process), and when combined with other types of data (in a hybrid recommendation process). The results of the experiments have been promising and shown that the stylistic visual features can be beneficial particularly in the hybrid recommendation process in the cold start scenario.