Service Recommendation Based on Topics and Trend Prediction

Web service recommendation is a challenging task when the number of services and service consumers are growing rapidly on the Internet. Previous research used information retrieve methods, such as keyword search and semantic matching, to speculate the intent of service consumers. The intent is matched with contents or topics of existing data. These methods help service consumers to select appropriate services according to their needs. However, service evolution over time and topic correlation has not been given sufficient attention. Thus we propose a service recommendation approach that is able to extract service evolution patterns from history statistic data and correlated topics from semantic service descriptions. To this end, time series prediction is used to obtain evolution patterns; Latent Dirichlet Allocation (LDA) is used to model the extracted topics. Experiments results show that our approach has higher precision than existing methods.