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

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.

AI-Powered Tracking for Sustainable Marine Ecosystem Resource Management Projects

“…Collecting additional video data from a wider range of oyster reefs in the New York Harbor, as well as other estuarine ecosystems, can improve the model's generalization capabilities and increase its applicability to diverse environments. Additionally, future work should focus on refining the model, addressing the current limitations, and exploring new data collection and analysis techniques with emerging technologies, such as big data analytics (Bag et al, 2022;Xie et al, 2022;Xing et al, 2022), blockchain (Buthelezi et al, 2022;Harshvardhan & Teoh, 2022;Qiu, 2022), edge computing (Liang et al, 2022;Huang et al, 2021), the Internet of Things (Almomani et al, 2021;Peng et al, 2021), and deep learning (Wu et al, 2022;Zhang & Song, 2022;Zhao, 2022). By continuously improving and expanding upon this research, we can contribute to the advancement of oyster restoration efforts, enhance ecological management practices, and further our understanding of oyster populations and their vital role in estuarine ecosystems.…”

Information System Success for Organizational Sustainability: Exploring the Public Institutions in Saudi Arabia

“…Notwithstanding this study's mixed results on the positive influence of information success on employees' perceptions of different types of sustainability, future researchers may test various information system variables using different measures of sustainability. For instance, industry 4.0 technology deployment like machine learning, data analytics, cloud computing, or robotics could foster more sustainable practices at the organizational level [92][93][94][95][96][97][98][99][100][101]. In addition, game theory approaches can offer models to understand how interactions between actors influence outcomes [102][103][104].…”

Relevancy or Diversity?

“…focus on developing algorithms to learn users' preferences and then to find relevant items to users (Kulkarni andRodd 2020, Zhang andSong 2021). Especially with the development of artificial intelligence (e.g., deep learning techniques), enhanced information retrieval and users' preferences learning have made RS more effective, so that it can provide more relevant items to users (Zhang et al 2019, Wu et al 2021).…”