Forecasting Consumer Interest in New Services Using Semantic-Aware Prediction Model: The Case of YouTube Clip Popularity

摘要

With intense increase in number of competing service providers in the information and communication sector, companies must implement mechanisms for forecasting consumer interest in new services. Common growth models provide the mechanisms for modelling and predicting acceptance of a certain service. However, they have two shortcomings: i) limited precision; and ii) a short, but yet existing, time delay. By using semantic reasoning for detecting similarities between services already on a market and ones that are just to be introduced, it is possible both to increase forecasting precision and eliminate the time delay caused by the need to collect a certain amount of data about the new service before a prediction can be made. The proposed semantic-aware prediction model is elaborated on a case of forecasting YouTube clip popularity.