The Association Between Film Industry Success and Prior Career History: A Machine Learning Approach

My thesis project is a means of understanding the conditions associated with success and failure in the American film industry. This is carried out by tracking the careers of several thousand actors and actresses, and the number of votes that their movies have received on IMDb. A fundamental characteristic of film career success is that of influence from prior success or failure—consider that an established “star” will almost certainly receive opportunities denied to an unknown actor, or that a successful actor with a string of poorly received films may stop receiving offers for desirable roles. The goal for this project is to to develop an understanding of how these past events are linked with future success. The results of this project show a significant difference in career development between actors and actresses—actors’ career trajectories are significantly influenced by a small number of “make or break” films, while actresses’ careers are based on overall lifetime performance, particularly in an ability to avoid poorly-received films. Indeed, negatively received films are shown to have a distinctly greater influence on actresses’ careers than those that were positively received. These results were obtained from a model using machine learning to find which movies from actors’ and actresses’ pasts tend to have the most predictive information. The parameters for which movies should be included in this set was optimized using a genetic learning algorithm, considering factors such as: film age, whether it was well-received or poorly-received, and if so, to what magnitude, and whether the film fits with the natural periodicity that many actors’ and actresses’ careers exhibit. Results were obtained following an extensive optimization, consisting of approximately 5000 evolutionary steps and 200,000 fitness evaluations, done over 125 hours.