Using Ontology for Personalized Mobile Message Computation

With the convenient of mobile device, mobile users are able to “pull” or “push” the mobile message at anytime and anywhere. The power of mobile message has turned into an important marketing power. However, how to utilize the availability and the quick-viewing convenience of mobile message to match the personal needs are not discussed. From the past studies, we notice that the studies of mobile recommendation focus more on location service and push service; few studies are focusing on personal mobile-message recommendation and its performance. In order to provide the personalized services, this research proposes three computation processes for conducting the mobile message recommendation. We apply ontology to construct the user preference profile and the message template containing the product information or service information. For personalized recommendation, the user preference profile and the message template are compared. The ontology matching techniques are proposed, they are: (1) Breadth-First-Matching (BFM), (2) Depth-First-Matching (DFM), and (3) Node-Index-Matching (NIM). The experiments are designed for examining the message “pull” service. To prove the proposed matching computations are applicable, the evaluation metrics use matching count for performance comparison. The experimental results show that the Node-Index-Matching (NIM) outperforms the other two matching methods and is good for the mobile message recommendation.