Constructing Top-k Routes with Personalized Submodular Maximization of POI Features
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We consider a practical top-k route problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination, that maximally match her needs on feature preferences and can be completed within a travel cost budget. One challenge is dealing with the personalized diversity requirement where each user has a different trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features). Another challenge is the large scale of the POI network and the great many alternative routes to search. We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route problem through an index structure for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints. We also present heuristic solutions and evaluate all the solutions on real life POI network data.
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