找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Artificial Intelligence and Machine Learning in the Travel Industry; Simplifying Complex Ben Vinod Book 2023 The Editor(s) (if applicable)

[復制鏈接]
查看: 42099|回復: 55
樓主
發(fā)表于 2025-3-21 18:38:03 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Intelligence and Machine Learning in the Travel Industry
期刊簡稱Simplifying Complex
影響因子2023Ben Vinod
視頻videohttp://file.papertrans.cn/163/162246/162246.mp4
發(fā)行地址Includes case studies of successful innovation in organisations.Addresses why AI has been less quickly adopted in the travel industry.Explores technological innovation in the travel industry
圖書封面Titlebook: Artificial Intelligence and Machine Learning in the Travel Industry; Simplifying Complex  Ben Vinod Book 2023 The Editor(s) (if applicable)
影響因子.Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high.?.The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice..
Pindex Book 2023
The information of publication is updating

書目名稱Artificial Intelligence and Machine Learning in the Travel Industry影響因子(影響力)




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry影響因子(影響力)學科排名




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry網(wǎng)絡公開度




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry網(wǎng)絡公開度學科排名




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry被引頻次




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry被引頻次學科排名




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry年度引用




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry年度引用學科排名




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry讀者反饋




書目名稱Artificial Intelligence and Machine Learning in the Travel Industry讀者反饋學科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 22:42:23 | 只看該作者
板凳
發(fā)表于 2025-3-22 04:17:06 | 只看該作者
Applying reinforcement learning to estimating apartment reference rents,nts adapt. The proposed RL model is trained and tested against real-world datasets of reference rents that are estimated with the use of one rules-based approach by two leading apartment management companies. Empirical results show that this RL-based approach outperforms the rules-based approach with a 19% increase in RevPAU on average.
地板
發(fā)表于 2025-3-22 07:15:16 | 只看該作者
5#
發(fā)表于 2025-3-22 10:59:27 | 只看該作者
6#
發(fā)表于 2025-3-22 16:52:08 | 只看該作者
Installing into an Existing Treeze forecast, and market share estimation. We also describe methodologies based on Machine Learning algorithms that can use to forecast these quantities and explain how they can be leveraged to improve pricing and revenue management practices.
7#
發(fā)表于 2025-3-22 20:26:46 | 只看該作者
Management and Monitoring Toolslearn traveler’s booking patterns and the latent progression of the booking curve. This solution can be leveraged by independent hoteliers in their revenue management strategy by comparing their behavior to the market.
8#
發(fā)表于 2025-3-22 23:11:44 | 只看該作者
Ausgew?hlte Aspekte aus weiteren Studienupon masses of data to systems that learn on the fly with little data, and from (b) centralized (even if in the cloud) machine learning to distributed artificial intelligence, and from (c) recommender systems to marketplace approaches.
9#
發(fā)表于 2025-3-23 03:48:54 | 只看該作者
10#
發(fā)表于 2025-3-23 09:31:11 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-29 13:02
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復 返回頂部 返回列表
探索| 界首市| 亚东县| 黄冈市| 康保县| 会昌县| 普陀区| 马山县| 镇江市| 娱乐| 南木林县| 玉林市| 若尔盖县| 肇庆市| 昌图县| 中宁县| 庆元县| 昌宁县| 伊金霍洛旗| 杭锦后旗| 萨嘎县| 永康市| 广安市| 博客| 五河县| 兴仁县| 达尔| 通化市| 汝南县| 雷山县| 林西县| 吉安市| 防城港市| 平谷区| 高青县| 红安县| 巫山县| 施甸县| 海兴县| 云南省| 麻江县|