Similarity-ranking method based on semantic computing for a context-aware system

摘要

Among the enormous variety of data in recent years, transportation data contain significant potential for understanding the information requirements and intention of passengers. In this paper, we propose a new information ranking method for passenger intention prediction and service recommendation. The method includes three main features, which include (1) predicting the intention of a used based on his/her current context, (2) selecting a subspace for service recommendation, and (3) ranking the services by the highest relevant order. By comparing the predicted results with a straightforward computation method, the experimental studies show the effectiveness and efficiency of the proposed method. The paper also describes the simplicity of our method over existing subspace selection methods.