Improving Recommender Engines for Video Streaming Platforms with RNNs and Multivariate Data

Sammanfattning: For over 4 years now, there has been a fierce fight for staying ahead in the so-called ”Streaming War”. The Covid-19 pandemic and its consequent confinement only worsened the situation. In such a market where the user is faced with too many streaming video services to choose from, retaining customers becomes a necessary must. Moreover, an extensive catalogue makes it even more difficult for the user to choose a movie from. Recommender Systems try to ease this task by analyzing the users’ interactions with the platform and predicting movies that, a priori, will be watched next. Neural Networks have started to be implemented as the underlying technology in the development of Recommender Systems. Yet, most streaming services fall victim to a highly uneven movies distribution, where a small fraction of their content is watched by most of their users, having the rest of their catalogue a limited number of views. This is the long-tail problem that makes for a difficult classification model. An RNN model was implemented to solve this problem. Following a multiple-experts classification strategy, where each classifier focuses only on a specific group of films, movies are clustered by popularity. These clusters were created following the Jenks natural breaks algorithm, clustering movies by minimizing the inner group variance and maximizing the outer group variance. This new implementation ended up outperforming other clustering methods, where the proposed Jenks’ movie clusters gave better results for the corresponding models. The model had, as input, an ordered stream of watched movies. An extra input variable, the date of the visualization, gave an increase in performance, being more noticeable in those clusters with a fewer amount of movies and more views, i.e., those clusters not corresponding to the least popular ones. The addition of an extra variable, the percent of movies watched, gave inconclusive results due to hardware limitations.