HTARF: A Hybrid Tourist Attraction Recommendation Framework for Trip Scheduling

As the users' demand for efficient and personalized services grows continuously, online tourism services are becoming more and more popular. However, many tourism products can only recommend existing tourist packages according to users' profile, which can hardly meet their personalized requirements. In this paper, we propose a novel attraction recommendation method, which takes advantages of quantities of information mined from attraction descriptions and tourist packages. The approach is modified on the basis of content matching and association rule mining and only requests a query from a user to recommend. In addition, the method is applied to our Smart Tourism Services Platform (STSP). Using attractions list offered by our method, a trip plan module of STSP will provide a personalized travel schedule. We examine our approach on a real-world dataset, and an online evaluation result shows that our approach is more effective and performs much better than baselines.