Cloud-mobi framework using hybrid AHP-ACO method for social interaction and travel planning

Advances in technology especially mobile computing has encouraged various travel recommendation applications to flourish in the market. Many just generate the itinerary according to the events and places of interest chosen by the users. Some involve higher level of intelligence where itineraries are recommended based on community review score and historical itineraries. However, very few have factored in the business operator layer in decision modeling. Involvement of business operator is currently at the minimal level where most of them are just providing business description and feedback to the comments. In this paper, we proposed a novel Cloud-Mobi framework to integrate three information layers from the community, the business operator and the user in itinerary recommendation. Business operator is given a more influential role in decision modeling by sharing their news and promotion plans. However, community reviews still make remarkable impact to avoid misleading information. We also enhance the framework by hybrid the AHP with ACO route optimization algorithm from our previous research to suggest an optimum itinerary and travelling path. A fusion of two levels decision modeling is proposed. The first level calculates interest score for places and events of interest based on user preference, business description and community review with Analytical Hierarchy Process (AHP). Second level generates the optimum travelling path using the Ant Colony Optimization (ACO) method of our past research. The paper includes an example of step-by-step AHP implementation in level 1 decision modeling to calculate the interest score. The implementation has shown that the proposed Cloud-Mobi framework is promising for travel recommendation applications. Our future work will focus on developing the travel recommendation system prototype to implement the proposed framework.