| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 102 === Due to the GPS devices getting popular, there are many applications appeared. Personal Navigation Assistant (PNA) is one of most popular applications. PNA service achieve mainly by GPS satellite navigation and the application (APP) of mobile device. It provides query service to search travel information like point of interesting (POI) and the routing navigation service.
Routing navigation service is the main function of PNA. At the beginning, it allows user to enter a specific address, or via travel information search service to obtain address of POI at begin. Then the system calculates the shortest route path between current location of user and requested address. This approach gets the shortest geographical distance of road path. It tries to get the most economical way to save the traveling time for travelers. It also provide the POI recommendation service based on shortest geographical distance and between each POI respective.
However, the distance is just one of the factors in recommend process. There are lot of factors can be consider in the PNA service. For example, the POI schedule service. The traditional schedule method recommend a POI at once according to distance between user and candidate POI list. It means user needs to repeat the operation to complete the whole travel schedule.
Although this method can provide the shortest path of distance, it did not consider other tourism demand such as lodging, dining, and shopping of users. It may bring some additional cost like re-move time and re-search information time of users. Therefore, other important factor such as preferences and needs of user in POI recommend process was also needs to be consider to enhance value of recommend result.
This research proposes a Recommendation Radius Method. Based on the user driving preference and expected driving time, the method produce a circle range, which is call “Recommend Radius”. The circle range is a first flitter to filter the distance of POI.
This research uses Travel ontology to create two sub-ontology: Ontology of user preferences and Ontology of Local area. The first sub-ontology is created by PNA device of operated process of users. It mainly collected the POI type of user preference to build. The other sub-ontology is created by recommend radius, it collected the POI type within the circle range based on user’s current coordinate. Next, this research propose a Binary Matching Method to matching these two sub-ontology. After that, we can find the difference between user preferences and its nearest POI of recommend radius.
Finally, this research also uses the additional needs of user like lodging or dinning to promote the recommend result. By these addition needs, we can reassessment the recommend result of first time, and fulfill users need and promote their satisfaction.
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