Image Driven Optimal Personalized Route Recommendation

2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)(2022)

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Optimal path recommendation relies on a trade-off between the route distance and the places of interest (POIs) present in the route, taking into account their number and their value. Here, a novel framework for personalized route recommendation is presented aiming at maximizing tourist satisfaction. The optimal path algorithm is driven by POI images which are embedded in a hypergraph, modeling the preferences of users for certain POIs. A ranking vector representing the relevance between the POIs and a user is computed and it is employed in the cost vector of the shortest path problem. An encoded graph helps to keep computational complexity at an acceptable level. Moreover, a modified version of the New Approach of Multiobjective A* search algorithm is proposed. This modified version, coined as MultiObjective NEgative Cyclic A* (MONECA), is a practical solution for multiobjective shortest simple path problem in graphs with a limited number of nodes, negative weights, and negative cycles.
image driven optimal personalized route recommendation,optimal path recommendation,route distance,tourist satisfaction,optimal path algorithm,POI images,ranking vector,cost vector,shortest path problem,multiobjective shortest simple path problem,places of interest,multiobjective negative cyclic A,MONECA,negative weights,negative cycles
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