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An Embedded GRASP-VNS based Two-Layer Framework for Tour Recommendation
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DOI：10.1109/TSC.2019.2963026.png)
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Abstract
En 中文
The advance of tour recommendation allows people to get well-fit route plans, which contain a sequence of Points of Interest (POIs) based on tourists' constraints and preferences. Large-scale POIs, called Super-POIs in this article, often contain multiple scenic spots and entrances. Tourists have to specify a suitable tour route inside Super-POI to obtain good tour experience. However, most of existing tour recommendation algorithms ignore the detailed information inside Super-POIs. By taking super-POIs into account, we propose Embedded Tour (eTOUR), a two-layer framework considering route design of POIs (Outer Model) and scenic routes inside the Super-POIs (Inner Model) respectively. To combine two models, an Embedded GRASP-VNS Algorithm is introduced based on an embedding strategy. For Outer Model, we apply Greedy Randomized Adaptive Search Procedure (GRASP) for route construction and Variable Neighborhood Search (VNS) for local improvement. Super-POI is treated as a meta node in outer route construction. For Inner Model, the optimal route inside Super-POI obtained by DFS-based Tree Search with Pruning is revised dynamically to adapt to the outer route. Furthermore, we discuss a special case in the Super-POI where a key graph is defined and treated as the must go route. We modify the solution of Chinese Postman Problem in this case to reduce the time complexity. Finally, experiments based on two real datasets demonstrate the effectiveness of our proposal.
Keywords:
Adaptation models
Search problems
Clustering algorithms
Trajectory
Planning
Transportation
Microsoft Windows
Tourist trip design problem
points of interest
variable neighborhood search
chinese postman problem
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