Method and apparatus for training path representation model

Patent metadata

Field	Value
Publication number	US-2022414689-A1
Application number	US-202217900649-A
Country	US
Kind code	A1
Filing date	Aug 31, 2022
Priority date	Jan 19, 2022
Publication date	Dec 29, 2022
Grant date	—

Abstract

A method and an apparatus for training a path representation model are provided. The method may include: acquiring at least one trajectory point of at least one user, where each trajectory point of each user includes a place passed by the user, a start time and a duration; inputting the at least one trajectory point of the at least one user into a pre-trained model to obtain a trajectory representation of each user; obtaining, for each user, a position of each trajectory point from the trajectory representation of the user by searching according to the start time and the duration of each trajectory point of the user; and adjusting a network parameter of the pre-trained model according to a difference between the place passed by each user and the position of each trajectory point obtained by searching, to obtain a path representation model.

First claim

What is claimed is:

1 . A method for training a path representation model, comprising:
acquiring at least one trajectory point of at least one user, wherein each trajectory point of each user comprises a place passed by the each user, a start time and a duration; inputting the at least one trajectory point of the at least one user into a pre-trained model to obtain a trajectory representation of each user; obtaining, for each user, a position of each trajectory point from the trajectory representation of the user by searching according to the start time and the duration of each trajectory point of the each user; and adjusting a network parameter of the pre-trained model according to a difference between the place passed by the each user and the position of the each trajectory point obtained by searching, to obtain the path representation model.

2 . The method according to claim 1 , further comprising:
acquiring a sample set, a sample in the sample set comprising a sample trajectory and a tag; and using respectively the sample trajectory and the tag in the sample set as an input and an expected output of the path representation model, to perform supervised training on the path representation model.

3 . The method according to claim 2 , further comprising:
dividing, for a target sample trajectory with a total duration exceeding a predetermined value in the sample set, the target sample trajectory into at least one segment according to a predetermined time interval; inputting, for each target sample trajectory, at least one segment of the target sample trajectory into the path representation model to obtain a representation of each segment of the target sample trajectory; and constructing, for each target sample trajectory, the representation of each segment into a sequence of representations of the target sample trajectory, and inputting the sequence and a time identifier corresponding to each segment into a sequence model, to output a sequence representation of the target sample trajectory.

4 . The method according to claim 3 , further comprising:
outputting the sequence representation of each target sample trajectory by a prediction model, to obtain a prediction result of the each target sample trajectory; and adjusting a network parameter of the sequence model according to a difference between the prediction result of the each target sample trajectory and a tag corresponding to the each target sample trajectory.

5 . The method according to claim 2 , wherein the tag comprises at least one of: a path category tag, an abnormal event tag, a next position tag, or a schedule tag.

6 . The method according to claim 1 , further comprising:
masking, according to a masking rule, places passed by the user in a part of the at least one trajectory point of the at least one user, to obtain at least one masked trajectory point; inputting the at least one masked trajectory point into the pre-trained model to obtain a mask position; and adjusting a network parameter of the pre-trained model according to a difference between the mask position and the masking rule, to obtain the path representation model.

7 . The method according to claim 1 , comprising:
acquiring to-be-analyzed user trajectory information; inputting the user trajectory information into the path representation model, to output a path representation; and inputting the path representation into a prediction model to output a prediction result.

8 . The method according to claim 7 , wherein the prediction result comprises at least one of: a path category, an abnormal event, a next position, or a schedule.

9 . An apparatus for training a path representation model, comprising:
at least one processor; and a storage device, wherein the storage device stores instructions executable by the at least one processor, and the instructions when executed by the at least one processor cause the at least one processor to perform operations comprising: acquiring at least one trajectory point of at least one user, wherein each trajectory point of each user comprises a place passed by the each user, a start time and a duration; inputting the at least one trajectory point of the at least one user into a pre-trained model to obtain a trajectory representation of each user; obtaining, for each user, a position of each trajectory point from the trajectory representation of the user by searching according to the start time and the duration of each trajectory point of the each user; and adjusting a network parameter of the pre-trained model according to a difference between the place passed by the each user and the position of the each trajectory point obtained by searching, to obtain the path representation model.

10 . The apparatus according to claim 9 , wherein the operations further comprise:
acquiring a sample set, a sample in the sample set comprising a sample trajectory and a tag; and using respectively the sample trajectory and the tag in the sample set as an input and an expected output of the path representation model, to perform supervised training on the path representation model.

11 . The apparatus according to claim 10 , wherein the operations further comprise:
dividing, for a target sample trajectory with a total duration exceeding a predetermined value in the sample set, the target sample trajectory into at least one segment according to a predetermined time interval; inputting, for each target sample trajectory, at least one segment of the target sample trajectory into the path representation model to obtain a representation of each segment of the target sample trajectory; and constructing, for each target sample trajectory, the representation of each segment into a sequence of representations of the target sample trajectory, and input the sequence and a time identifier corresponding to each segment into a sequence model, to output a sequence representation of the target sample trajectory.

12 . The apparatus according to claim 11 , wherein the operations comprise:
outputting the sequence representation of each target sample trajectory by a prediction model, to obtain a prediction result of the each target sample trajectory; and adjusting a network parameter of the sequence model according to a difference between the prediction result of the each target sample trajectory and a tag corresponding to the each target sample trajectory.

13 . The apparatus according to claim 10 , wherein the tag comprises at least one of: a path category tag, an abnormal event tag, a next position tag, or a schedule tag.

14 . The apparatus according to claim 9 , wherein the operations further comprise:
masking, according to a masking rule, places passed by the user in a part of the at least one trajectory point of the at least one user, to obtain at least one masked trajectory point; inputting the at least one masked trajectory point into the pre-trained model to obtain a mask position; and adjusting a network parameter of the pre-trained model according to a difference between the mask position and the masking rule, to obtain the path representation model.

15 . The apparatus according to claim 9 , wherein the operations further comprise:
acquiring to-be-analyzed user trajectory information; inputting the user trajectory information into the path representation model, to output a path representation; and a predicting unit, configured to input the path representation into a prediction model to output a prediction result.

16 . The apparatus according to claim 15 , wherein the prediction result comprise

Assignees

• Beijing Baidu Netcom Sci & Tech Co Ltd

Inventors

• Zhang Qi
• Zhu Hengshu
• Wang Peng
• Liu Hao
• Xiong Hui

Classifications

• G06F16/9537

  Spatial or temporal dependent retrieval, e.g. spatiotemporal queries · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/084 Primary

  Backpropagation, e.g. using gradient descent · CPC title

• G06Q30/0201 Primary

  Market modelling; Market analysis; Collecting market data · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title