An Integration Of AHP-ACO Technique For Social Interaction And Travel Planning

The current web and mobile computing technologies have encouraged all sorts of applications mushroom in the market. However, most of the application that available does not integrate the place recommendation and route planning. Besides that, improving the processing speed of the algorithm is also another challenge of this research. Thus, the objectives of this research is to integrate the place recommendation based on profile preference using Analytic Hierarchy Process (AHP) method and route planning using ACO method. The second objective of this research is to enhance the processing speed of the proposed AHP-ACO technique in generating the optimum route plan. This study presents the integration methods of AHP algorithm for point of interest decision-making and ACO and rule-based algorithms for route optimization. AHP interest scores based on user preferences, business information and community reviews are used to model decision making. ACO and rule-based algorithms are used to arrange the itinerary of the place of interest that either has been chosen by the user or recommended by the system.The integration AHP-ACO method has been enhanced to reduce the execution time from 5 minutes to 30 seconds for 7 days trip planning. Object Oriented Software Engineering(OOSE) methodology has been used to build the mobile recommender system prototype and web application prototype. Questionnaires have been distributed to collect user feedback. The results show that the integration method is promising for helping the user in making decisions and itinerary arrangements

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