基于情感分析的冷启动推荐和深度神经学习(SACNN):一种新的流行病旅行推荐方法

Jasmine Samraj, N. Menaka
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引用次数: 1

摘要

旅行一直是一件很平常的事情,人们旅行的特殊原因,如商务会议,度假,医疗紧急情况,聚会等。但在covid-19形势下旅行一直是一个令人担忧的问题,在各个城市都有很多限制措施来控制疫情。为了控制用户在旅行过程中的流行,并方便获取信息,我们使用了与社交媒体相关的旅行推荐。在所提出的系统中,系统分析用户的社交媒体账户来收集信息并更新旅行历史。因此,当新用户浏览任何旅行更新时,服务器将经历一个验证过程并给出相应的建议。为了推荐目的,提出的系统引入了一种新的机制,称为“基于情感分析的冷启动推荐与深度神经学习(SACNN)”。该方法存储并保存了所有最近的旅行和covid-19相关的详细信息,供用户查看。此外,该安全系统使假识别分类器能够检测社交媒体中的假信息。所提出的理论将提供比现有的其他性能更高的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentimental Analysis Based on Cold-Start Recommendation with Deep Neural Learning (SACNN): A Novel Approach for Travel Recommendation in Pandemic
Travelling is always being a usual thing where people travel for particular reasons such as business meetings, vacation, medical emergencies, and get-together parties, etc. But travelling in the covid-19 situation has been a concern, where there are lots of restrictions are allotted in various cities to control the pandemic situation. To control the pandemic among the user during travelling and to obtain easy information access ‘travel recommendation correlated with social media is used. In the proposed system the system analyzes the user’s social media accounts to gather information and updates the travel history. Hereby, when a new user surfs for any travel updates the server undergoes a validation process and suggests accordingly. For recommendation purposes, the proposed system introduces a new novel mechanism named ‘Sentimental Analysis Based Cold-start recommendation with Deep Neural Learning (SACNN)’. In this method, all the recent travel and covid-19 related details are stored and saved for user check. Further, the system for security enables a fake identification classifier to detect fake information in social media. The proposed theory will provide better accuracy rate than the existing other performances.
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