Series and cinematic universes
The model is required to test well with TV shows, mini-series and cinematic universes, and updated accordingly
Marketing campaigns, i.e., news, are the key factor that contributes to the success of movies. However, we have collected all data available, which means their usage is limited
AI will factor in data over time, for example, changes in genre, director popularity and the like
Data collection and analysis
We are now mostly employing IMDb in our analyses and breakdowns, but there are plans to include other sources and databases down the road: it will improve analytics drastically
Difference makes all the difference
The model is supposed to learn what near-identical movies are and find its way around remakes
Uncertainty quantification is the staple here, so we need to quantify both aleatoric and epistemic uncertainty (uncertainty of the model and data).
Advanced architecture model
Our graph database only features data on relationships between movie makers and movies. We need to allow a natural representation of higher-order relationships, so advancement is a must.
The generator now produces a sample, which is then uploaded to the classifier; it’s far from perfect, but that’s something we’re working on