A Package Generation and Recommendation Framework Based on Travelogues

Abstract

Tourism has become the world's largest economy industry. More and more people share their travelogues on travel websites. Recommender system is an effective tool to provide travel services (e.g., Landscapes selection) for tourists. Many recommender systems are based on travel data that are supplied by travel agencies, and provide travel packages from a fixed package set, which bring two challenges for travel package recommender system. One is how to generate more travel packages. The other is how to measure more fine-grained user similarity. To address these challenges, we develop a package generation and recommendation framework to help travelers select landscapes. Firstly, we propose a Fuzzy Clustering based Package Generation algorithm (FCPG) to generate new travel packages to improve the overall recommendation effectiveness. Then, we develop a Dual Topic Model based Package Recommendation algorithm (DTMPR). It considers two user-related topics (travel seasons and areas), and provides more fine-grained user similarity measure. Experimental results show the superiority of our framework in comparison with the state-of-the-art methods.