Demand Prediction Based on Social Context for Mobile Content Services

The recent enhancement of mobile devices and wireless networks has enabled content services in mobile environments. Demand prediction is a traditional but powerful technique used for content services. However, it is hard to predict local demand in mobile environments because it depends not only on just user preference and the popularity of common content but also on other factors; users request content related to their locations; moreover, they are interested in the content uploaded by their friends who visited the location before them. Thus, we need to consider the context with multiple factors affecting the demand. In addition, we also need to consider the sequence of contexts.We call a sequence of contexts social context. This makes it difficult to predict local demand from users. In this paper, we propose a novel demand prediction engine that extracts local demand depending on social context and estimates what content will be requested there. To extract the demand, we use a log database and a pattern-matching technique in our prediction engine. To validate our prediction engine, we apply a prefetching service using the engine to a mobile content service.