Image Driven Optimal Personalized Route Recommendation

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

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摘要
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.
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关键词
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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