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논문 상세정보

협업적 추천 기반의 여행 계획 시스템

Multi-day Trip Planning System with Collaborative Recommendation

초록

여행을 계획하는 일은 매우 복잡하고 많은 시간을 필요로 한다. 여행 계획을 정할 때에는 보통 관심 지점(point of interests, POIs)을 선택하고 그에 따른 다양한 제약 조건들을 고려하여 일정을 계획 한다. 관심 지점을 선정할 때 친구들에게 의견을 묻거나 인터넷에서 직접 정보를 찾으며 여행사의 도움을 받기도 한다. 하지만 이러한 방법들은 다음과 같은 어려움이 있다. 친구들에게 의견을 묻는 경우에는 친구들이 방문해 보지 못한 장소에 대한 정보를 얻기 어렵고 인터넷에서 정보를 찾는 경우에는 오히려 너무 많은 여행 정보들 때문에 필요한 정보를 탐색하고 정리하는데 많은 시간이 필요하며 여행사의 도움을 받을 때에는 여행 일정이 여행을 제공해주는 업체들 쪽으로 편중될 우려가 있다. 이러한 문제를 해결하기 위해 본 논문에서는 여행 일정 계획 시스템인 CYTRIP을 제안한다. CYTRIP은 웹 기반의 추천 시스템으로써, 여행 정보를 공유할 수 있는 공간을 제공하고, 이를 통해 참여자들의 집단 지성에 따른 관심 지점을 추천 받는다. 그리고 PDDL3를 통해 추천된 지점들의 시간적, 공간적 제약조건 따라 여행 일정이 자동으로 생성되며 이렇게 생성된 일정은 지도 위에 표시되어 사용자에게 제공된다. 여행을 계획할 때에 정해진 기간 동안 모든 추천 관심지점을 방문할 수 없는 경우가 발생한다. 이러한 문제를 피하기 위해 정해진 시간에 방문 가능한 관심 지점들의 후보 집합을 선택하고 이 후보 집합들에 대한 여행 일정을 생성한다. 제안하는 시스템의 성능평가를 위해 사용자 평가를 실시하였다. 사용자 평가를 위해 한국관광공사에서 제공하는 데이터를 활용하였고 평가 결과 제안하는 시스템이 여러 참여자들의 집단 지성을 통해 여행 일정을 계획하는데 유용하다는 것을 알 수 있었다.

Abstract

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

참고문헌 (23)

  1. Benton, J., M. B. Do, and S. Kambhampati, "Over-subscription planning with numeric goals," International Joint Conferences on Artificial Intelligence Organization, (2005), 1207-1213. 
  2. Borras, J., A. Moreno, and A. Valls, "Intelligent tourism recommender systems: A survey," Expert Systems with Applications, Vol.41, No.16(2014), 7370-7389. 
  3. Chen, G., S. Wu, J. Zhou, and A. K. Tung, "Automatic Itinerary Planning for Travelling Services," IEE Transaction on Knowledge and Data Engineering, Vol.26, No.3(2014), 514-527. 
  4. Edelkamp, S., S. Jabbar, and M. Nazih, "Large-Scale Optimal PDDL3 Planning with MIPS-XXL," International Planning Competition Booklet, (2006), 28-30. 
  5. Fox, M. and D. Long, "PDDL2.1 : An Extension to pddl for Expressing Temporal," Journal of Artificial Intelligence Research, Vol.20, No.1(2003), 61-124. 
  6. Garcia-Olaya, A., T. Rosa, and D. Borrajo, "Using the relaxed plan heuristic to select goals in oversubscription planning problems," Advances in Artificial Intelligence, (2011), 183-192. 
  7. Gavalas, D., C. Konstantopoulos, and K. Mastakas, "A survey on algorithmic approaches for solving tourist trip design problems," Journal of Heuristics, Vol.20, No.3(2014), 291-328. 
  8. Gerevini, A. and D. Long, "Plan constraints and preferences in PDDL3," The Language of the fifth International Planning Competition-Technical Report, University of Brescia, 2005. 
  9. Kurata, Y. and T. Hara, "CT-planner4: Toward a more user-friendly interactive day-tour planner," Information and Communication Technologies in Tourism, (2013), 73-86. 
  10. Hsu, C. W., B. W. Wah, R. Huang, and Y. Chen, "New features in SGPlan for handling preferences and constraints in PDDL3.0," Proceedings of the Fifth International Planning Competition, (2006), 39-41. 
  11. Kurashima, T., T. Iwata, G. Irje, and k. Fujimura, "Travel route recommendation using geotags in photo sharing sites," Proceedings of the 19th ACM international conference on Information and knowledge management, (2010), 579-588. 
  12. Lee, J. H. and M. M. Sohn, "Traveling Product Bundling on Web Service Composition in Ubiquitous Computing Environment," Journal of Intelligence and Information Systems, Vol.12, No.2(2006), 49-65. 
  13. Lee, J. H. and M. M. Sohn, "Framework for Information Integration and Customization Using Ontology and Case-based Reasoning," Journal of Intelligence and Information Systems, Vol.15, No.4(2009), 141-158. 
  14. Li, X., "Multi-day and multi-stay travel planning using geo-tagged photos" Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, (2013), 1-8. 
  15. Manikonda, L., T. Chakraborti, S. De, K. Talamadupula, and S. Kambhampati, "AI-MIX: using automated planning to steer human workers towards better crowdsourced plans," Second AAAI Conference on Human Computation and Crowdsourcing, (2014), 42-43. 
  16. Pednault, E. P. D., "ADL: Exploring the middle ground between STRIPS and the situation calculus," Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, (1989), 324-332. 
  17. Fikes, R. E. and N. J. Nilsson, "STRIPS: A new approach to the application of theorem proving to problem solving," Artificial Intelligence, Vol.2, No.3-4(1971), 189-208. 
  18. Sebastia, L., I. Garcia, E. Onaindia, and C. Guzman, "e-Tourism: a tourist recommendation and planning application," International Journal on Artificial Intelligence Tools, Vol.18, No.5(2009), 717-738. 
  19. Smith, D. E., "Choosing Objectives in Over-Subscription Planning," International Conference on Automated Planning and Scheduling, Vol.4(2004), 393-401. 
  20. Sylejmani, K. And A. Dika, "Solving touristic trip planning problem by using taboo search approach," International Journal of Computer Science Issues, Vol.8, No.5(2011), 139-148. 
  21. Vansteenwegen, P., W. Souffriau, G. V. Berghe, and D. V. Oudheusdena, "Iterated local search for the team orienteering problem with time windows," Computers and Operations Research, Vol.36, No.12(2009), 3281-3290. 
  22. Yu, Y. H., S. J. Cha, and G. S. Jo, "Hybrid Heuristic Applied by the Opportunity Time to Solve the Vehicle Routing and Scheduling Problem with Time Window," Journal of Intelligence and Information Systems, Vol.15, No.3(2009), 137-150. 
  23. Zhang, H., E. Law, R. C. Miller, K. Z. Gajos, D.C. Parkes, and E. Horvitz, "Human computation tasks with global constraints," Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (2012), 217-226. 

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