Forecasting the acceptance of new information services by using the semantic-aware prediction model

With the constantly increasing competition on the information service market, service providers should enhance their business processes by introducing new mechanisms. These novel mechanisms must include almost real-time detection of business opportunities (as well as possible failures), necessary resource prediction, and finally profit forecasting. Presented challenges can be tackled by using growth models for service acceptance prediction. However, common growth models have certain shortcomings when it comes to forecasting consumer interest in new services. Two main shortcomings are: i) limited precision; and ii) a short, but yet existing, time delay. Possible solution that minimizes the specified shortcomings is semantic reasoning, which can be used for detecting similarities between services already on the market and ones that are just to be introduced. Consequently, it becomes possible both to increase forecasting precision and eliminate time delay caused by the need to collect a certain amount of data about the new service before any prediction can be made. Our approach, the semantic-aware prediction model, can thus replace the common subjective service similarity approximation approach. Elaboration and verification of the semantic-aware prediction model are conducted on a case of forecasting YouTube clip popularity.