[Show abstract][Hide abstract] ABSTRACT: In an E-Commerce environment with appeals in efficiency, the ontology constituted by Mobile Message and the time spent on conveying messages to appropriate users, will attract a high degree of attention. Nonetheless current study environment still lacks significant exploration in this field. In view of this, the study established mobile message template using Generally in the field of ontology, much emphasis is placed on how to apply ontology information; whereas few studies explore the efficacy of matching while more emphasize on how to match with respect to ontology matching. Under current mobile-commerce environment, the content of messages conveyed by shops mostly renders in texts. Consequently efficiency becomes the most important key to consideration. This article will conduct structural similarity matching through ontology structure, exploring the efficacies generated via different methods, and matching the two types of traditional ontology: Performance assessment was conducted using Breadth-First-Matching (BFM), Depth-First-Matching (DFM) and Node-Index-Matching (NIM) proposed by the study, in order to determine the most suitable method based on the numbers of matching. The experimental data showed that Node-Index-Matching (NIM) proposed by the study had significantly optimal performance on efficacy assessment, which consequently is more suitable for use in mobile environment with large quantity of messages.
[Show abstract][Hide abstract] ABSTRACT: 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.