Scenic spot recommendation method based on gated recurrent unit neural network

A technology of cyclic unit and neural network, applied in the field of neural network and intelligent recommendation, machine learning, can solve problems such as startup difficulties, ignoring the hidden semantics of travel trajectories, cold start, etc., to reduce computing time, improve recommendation effects, and reduce computing volume effect

Active Publication Date: 2019-07-09
GUILIN UNIV OF ELECTRONIC TECH
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AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is the difficult problem of starting in the prior art:

Method used

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  • Scenic spot recommendation method based on gated recurrent unit neural network
  • Scenic spot recommendation method based on gated recurrent unit neural network
  • Scenic spot recommendation method based on gated recurrent unit neural network

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Embodiment 1

[0042] This embodiment provides a method for recommending scenic spots based on a gated recurrent unit neural network, such as figure 1 .

[0043] Specifically include the following steps:

[0044] Step 1, use the web crawler to collect user ID, scenic spot name and play time attribute data, and perform preprocessing, and then generate a travel sequence according to each user's browsing time of the scenic spot;

[0045] Step 2, input the tourism sequence into the gated recurrent unit neural network learning model at the same time, and add effectively extracted negative examples on the basis of other scenic spots in the same batch as negative examples for training;

[0046] Step 3, find the scenic spot with the highest score among all scenic spots and compare the scores of all negative scenic spots with it and assign weights according to the ratio;

[0047] Step 4. Calculate the loss function according to the weight ratio of all negative examples, and use Pairwise to update t...

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Abstract

The invention relates to a scenic spot recommendation method based on a gated recurrent unit neural network. With the method, technical problems such as difficult start, data sparsity and neglect of implicit semantics in gaming trajectories are solved. The method comprises the following steps: step 1, collecting tourism data < uj1, sj2 and vj3 >, preprocessing the tourism data, and generating a tourism sequence T for representing the tourism track according to all tourism data of the jth tourist and a time sequence; step 2, inputting the tourism sequence in the step 1 into a gating circulationunit neural network, modeling tourism data through the gating circulation unit neural network, and establishing a gating circulation unit neural network learning model; step 3, taking the tourism sequence T in the step 1 as a data set, inputting the data set into the gating circulation unit neural network learning model in the step 2, and taking other scenic spots in the same batch as negative examples for training; and step 4, defining a loss function, updating a recommendation list, and finishing scenic spot recommendation, thereby better solving the problem. The method can be applied to scenic spot recommendation.

Description

technical field [0001] The invention relates to the fields of machine learning, neural network and intelligent recommendation, in particular to a method for recommending scenic spots based on a maximum negative example gated recurrent unit neural network. Background technique [0002] In recent years, the tourism industry has become one of the most important industries, and the Internet has developed faster and faster, and it has also entered people's lives. In terms of tourism and leisure, people began to inquire about tourism information through the Internet and screen out the information they like. The increase in users makes the amount of data on the Internet exponentially increase, and the increase in the amount of data requires users to spend a lot of time screening the attractions they are interested in so that it is difficult to choose a suitable travel route. Therefore, recommending a suitable travel route for each user is a problem that needs to be solved at prese...

Claims

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Application Information

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IPC IPC(8): G06Q50/14G06F16/9535
CPCG06Q50/14
Inventor 常亮张舜尧孙磊孙彦鹏匡海丽
Owner GUILIN UNIV OF ELECTRONIC TECH
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