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: Learning to Rank for Information Retrieval and Natural Language Processing; Hang Li Book 2011 Springer Nature Switzerland AG 2011

[復(fù)制鏈接]
查看: 6834|回復(fù): 38
樓主
發(fā)表于 2025-3-21 18:43:23 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Learning to Rank for Information Retrieval and Natural Language Processing
編輯Hang Li
視頻videohttp://file.papertrans.cn/584/583011/583011.mp4
叢書名稱Synthesis Lectures on Human Language Technologies
圖書封面Titlebook: Learning to Rank for Information Retrieval and Natural Language Processing; Hang Li Book 2011 Springer Nature Switzerland AG 2011
描述Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings.Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation
出版日期Book 2011
版次1
doihttps://doi.org/10.1007/978-3-031-02141-1
isbn_ebook978-3-031-02141-1Series ISSN 1947-4040 Series E-ISSN 1947-4059
issn_series 1947-4040
copyrightSpringer Nature Switzerland AG 2011
The information of publication is updating

書目名稱Learning to Rank for Information Retrieval and Natural Language Processing影響因子(影響力)




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing影響因子(影響力)學(xué)科排名




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing網(wǎng)絡(luò)公開度




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing被引頻次




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing被引頻次學(xué)科排名




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing年度引用




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing年度引用學(xué)科排名




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing讀者反饋




書目名稱Learning to Rank for Information Retrieval and Natural Language Processing讀者反饋學(xué)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 2025-3-22 00:11:06 | 只看該作者
Learning to Rank for Information Retrieval and Natural Language Processing978-3-031-02141-1Series ISSN 1947-4040 Series E-ISSN 1947-4059
板凳
發(fā)表于 2025-3-22 00:25:12 | 只看該作者
地板
發(fā)表于 2025-3-22 04:47:34 | 只看該作者
Learning for Ranking Aggregation,le ranking, which is better than any of the original rankings in terms of an evaluation measure. Learning for ranking aggregation is about building a ranking model for ranking aggregation using machine learning techniques.
5#
發(fā)表于 2025-3-22 12:31:26 | 只看該作者
Methods of Learning to Rank,ank [114, 115], RankNet [11], LambdaRank [12, 32], ListNet & ListMLE [14, 104], AdaRank [108], SVM MAP [111], and SoftRank [43, 95], and three methods for ranking aggregation, including Borda Count [34], Markov Chain [34], and CRanking [63].
6#
發(fā)表于 2025-3-22 14:00:09 | 只看該作者
Applications of Learning to Rank,ocument retrieval, expert search, definition search, meta-search, personalized search, online advertisement, collaborative filtering, question answering, key phrase extraction, document summarization, and machine translation.
7#
發(fā)表于 2025-3-22 17:30:16 | 只看該作者
8#
發(fā)表于 2025-3-22 23:59:30 | 只看該作者
9#
發(fā)表于 2025-3-23 04:14:52 | 只看該作者
Synthesis Lectures on Human Language Technologieshttp://image.papertrans.cn/l/image/583011.jpg
10#
發(fā)表于 2025-3-23 07:14:27 | 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(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-8 20:12
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(fù) 返回頂部 返回列表
鄢陵县| 南宁市| 永吉县| 内江市| 灵台县| 江山市| 玉门市| 房山区| 彰化县| 米林县| 广平县| 彩票| 吴桥县| 迁西县| 工布江达县| 当涂县| 密山市| 静宁县| 余姚市| 定西市| 霍林郭勒市| 柘城县| 奈曼旗| 洱源县| 德惠市| 锦屏县| 托克托县| 明光市| 莱西市| 贵阳市| 运城市| 梨树县| 金坛市| 邹平县| 花莲县| 南京市| 定南县| 奉新县| 宁陵县| 朝阳市| 宝应县|