Personalized route recommendation method based on A star search and deep learning

Abstract

The invention discloses a personalized route recommendation method based on A star search and deep learning, which comprises the following steps: step 1, taking a historical track data set D, a starting point ls, an end point ld, a departure time b and a user u as input, and then inputting a recurrent neural network; step 2, modeling a cost function (n) from the starting point to the current n node and a cost function h (n) from the current n node to the terminal point; and step 3, in the process of finding the optimal path, extending one node each time, using the f (n) to evaluate the score of the node, and recommending the personalized optimal path track p *. The invention provides a personalized route recommendation method based on A star search and deep learning. In the personalized route recommendation method, a transfer rule between track points is learnt through a recurrent neural network, an attention mechanism based on historical data is utilized to help to learn the current cost in the A * algorithm, and finally a graph attention neural network is introduced to model the future cost in the A * algorithm.

Description

technical field [0001] The invention relates to the technical field of trajectory data mining, and more specifically relates to a personalized route recommendation method based on A-star search and deep learning. Background technique [0002] The biggest difference between the current route recommendation solution and the past route recommendation solution is that the existing solutions are all data-driven. By mining and analyzing a large amount of historical trajectory data, users' interest needs can be modeled very accurately. [0003] However, it is difficult for the current schemes to make full use of trajectory data. Many schemes are only performing simple statistics on the data, and then search for the so-called most popular or least time-consuming trajectory through commonly used heuristic search algorithms. However, none of these solutions can capture the user's characteristics and cannot meet the individual needs of the user. [0004] On the other hand, with the r...

AI Technical Summary

Problems solved by technology

[0003] However, it is difficult for the current schemes to make full use of trajectory data. Many schemes are only performing simple statistics on the data, and then search for the so-called most popular or least time-consuming trajectory through the commonly used heuristic search algorithm.

However, none of these solutions can capture the characteristi...

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