Intelligence traveling schedule recommender based on commonsense reasoning algorithm

Traveling-schedule (TS) arrangement is a classical ill-define problem which lacks of structure and fulfills uncertainty and dynamic complexity. In general, there are two ways to resolve TS arrangement (TSA) problem, including: package tourism provide by travel agency who arrange entire traveling program. The other is independent tourism that travelers should collect information and arrange all traveling-detail themselves. Nowadays, independent tourism is getting popular and may instead of total package one due to tourism flexibility and customization. To cater for independent tourism customer, many travel agencies have already developed recommender system to provide online traveler with particular tourism packages according to their query conditions. However, such recommendation result usually become involve in package tourism advertisements and lack of flexibility. Additionally, such recommender mechanism can not replicate important word-of-mouse effect about traveling experience. Thus, the recommender mechanism should be revised for TSA problem solving. This research proposed an intelligence traveling recommender (iTR) system based on commonsense reasoning (CR) algorithm. iTR includes two reasoning processes, the general reasoning and the exception one. Furthermore, iTR consists of three intelligent agents and three automatic mechanisms enable user constantly refine and revise suggested traveling-schedule by iTR. CR is an appropriate methodology to deal with TSA problem because of CR can replicate human decision process actually. Finally, a demonstration TSA scenario is presented to illustrate the effect and feasibility of proposed iTR recommender architecture.