No abstract

Tourism recommendation systems play a vital role in providing useful travel information to tourists. However, existing systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to their lack of onsite travel behavioral data and related route mining algorithms. To this end, a novel travel route recommendation system is proposed, which collects tourist onsite travel behavior data automatically regarding a specific POI based on smart phone and IoT technology. Then, the proposed system preprocesses the behavior data to transform raw behavior sequences into Tourist-Behavior pattern sequences. Subsequently, the system discovers frequent travel routes from the generated pattern sequences by using an original route mining algorithm, named Tourist-Behavior PrefixSpan. Finally, a route-recommending method is designed to search and rank tangible travel routes according to the querying tourist's profile and constraint. The experimental results demonstrate that the proposed system is efficient and effective in recommending POI-oriented tangible travel routes considering tourists' route constraints and personal profile while ensuring that the suggested routes have considerable route values.

No abstract

No abstract

No abstract