PD-TRP: A Service Composition Approach for the Personalized and Optimized Door-to-Door Travel Plan Recommendation
Yiqun Hua, Jian Cao, Qi Gu, Yudong Tan · 2017
In order for travelers to arrive at their destination comfortably, it is important that they plan their trip carefully. Our motivation in this paper is to free the traveler from tedious planning tasks. Several studies and applications have been proposed to help travelers plan their trip according to explicit preferences, but most ignore implicit preferences. For the first time, to the best of our knowledge, we propose a Personalized and Optimized Door-to-door TRavel Planning two-stage approach named PD-TRP, which combines different kinds of transportation services to provide high quality and cost-effective travel plan recommendations, taking into consideration individual explicit and implicit preferences in real time. In order to recommend a set of suitable plans with explicit attribute values, we propose an efficient heuristic search algorithm to generate the top-K candidate plans, and rank them according to the information entropy of implicit preferences discovered from the travelers' previous travel behaviour. We simulate information on transportation services, crawled from the Internet, on 2000 travelers and their past travel behaviour over the last three years, collected from an online travel company, and extensive experimental results show the two-stage approach PD-TRP can effectively improve the degree of satisfaction of the travelers.