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...

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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 characteristics of users and cannot meet the individual needs of users.

[0004] On the other hand, with the rise of deep learning, although the deep learning model can capture the complex laws of the data and can integrate multiple information to help, the ordinary sequence model based on the recurrent neural network is not suitable for route recommendation search question

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