Top-k Route Search through Submodularity Modeling of Recurrent POI Features

TL;DR

This paper introduces a submodularity-based model for top-k route search that considers personalized feature preferences, offering optimal and heuristic solutions evaluated on real data.

Contribution

It models personalized diversity with submodular functions and provides an optimal search algorithm with pruning strategies for large-scale POI maps.

Findings

Optimal solution with indexing and pruning strategies

Heuristic solutions that perform well in practice

Evaluation on real-world data demonstrating effectiveness

Abstract

We consider a practical top-k route search 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 and limited in a cost budget, that maximally match her needs on feature preferences. One challenge is dealing with the personalized diversity requirement where users have various 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 map 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 search problem through indices 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 promising heuristic solutions and evaluate all the solutions on real life data.