Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

  • Zhang X
  • Song Y
N/ACitations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

Cite

CITATION STYLE

APA

Zhang, X., & Song, Y. (2022). Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management. Journal of Global Information Management, 30(7), 1–16. https://doi.org/10.4018/jgim.296145

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free