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當(dāng)前位置: 首頁(yè)出版圖書科學(xué)技術(shù)計(jì)算機(jī)/網(wǎng)絡(luò)數(shù)據(jù)庫(kù)數(shù)據(jù)庫(kù)設(shè)計(jì)/管理圖計(jì)算與推薦系統(tǒng)

圖計(jì)算與推薦系統(tǒng)

圖計(jì)算與推薦系統(tǒng)

定 價(jià):¥99.00

作 者: 劉宇 著
出版社: 機(jī)械工業(yè)出版社
叢編項(xiàng):
標(biāo) 簽: 暫缺

ISBN: 9787111736967 出版時(shí)間: 2023-12-01 包裝: 平裝-膠訂
開本: 16開 頁(yè)數(shù): 字?jǐn)?shù):  

內(nèi)容簡(jiǎn)介

  這是一本全面講解圖計(jì)算、知識(shí)圖譜及其在推薦系統(tǒng)領(lǐng)域應(yīng)用的專著,為讀者基于神經(jīng)網(wǎng)絡(luò)構(gòu)建推薦系統(tǒng)提供了詳細(xì)指導(dǎo),是作者在相關(guān)領(lǐng)域10余年經(jīng)驗(yàn)的總結(jié)。掌握本書內(nèi)容,讀者可開發(fā)出能處理多模態(tài)數(shù)據(jù)的推薦算法系統(tǒng),提供更豐富和準(zhǔn)確的推薦體驗(yàn)。本書主要內(nèi)容分為兩篇。第一篇 圖數(shù)據(jù)以及圖模型(第1-3章)對(duì)圖數(shù)據(jù)、圖神經(jīng)網(wǎng)絡(luò)、知識(shí)圖譜的基礎(chǔ)知識(shí)進(jìn)行了梳理,幫助讀者掌握著3項(xiàng)技術(shù)的關(guān)鍵原理與算法,為后面的學(xué)習(xí)打下基礎(chǔ)。第二篇 推薦系統(tǒng)(第4-9章)首先介紹了推薦系統(tǒng)的架構(gòu),包括邏輯架構(gòu)、技術(shù)架構(gòu)和數(shù)據(jù)建模,以及基于GNN的推薦系統(tǒng)架構(gòu);然后詳細(xì)講解了如何基于GNN構(gòu)建推薦系統(tǒng),以及基于圖的推薦算法;再接著講解了知識(shí)圖譜在推薦系統(tǒng)中的應(yīng)用以及相關(guān)的算法模型;最后,探討了推薦系統(tǒng)領(lǐng)域當(dāng)前的熱點(diǎn)問題、研究方向以及工業(yè)級(jí)推薦系統(tǒng)領(lǐng)域的核心難題本書注重實(shí)戰(zhàn),故理論知識(shí)簡(jiǎn)練且極具針對(duì)性,包含大量實(shí)戰(zhàn)案例,圖文并茂,易于閱讀。

作者簡(jiǎn)介

  劉 宇資深A(yù)I技術(shù)專家和搜索與推薦領(lǐng)域?qū)<?,曾在多家互?lián)網(wǎng)公司擔(dān)任資深算法專家、技術(shù)總監(jiān)以及技術(shù)VP,現(xiàn)擔(dān)任某創(chuàng)業(yè)公司CTO。在人工智能和信息檢索領(lǐng)域有10余年開發(fā)經(jīng)驗(yàn),對(duì)主流的推薦、搜索、聊天機(jī)器人、大模型等技術(shù)、產(chǎn)品與解決方案都有深入研究,尤其擅長(zhǎng)用簡(jiǎn)單高效的方法解決公司的數(shù)智化問題。項(xiàng)目經(jīng)驗(yàn)豐富,曾成功主導(dǎo)多個(gè)電商算法項(xiàng)目的落地和實(shí)施,參與完成多個(gè)推薦系統(tǒng)從0到1的搭建。曾在多家單位獲得個(gè)人開發(fā)優(yōu)秀貢獻(xiàn)獎(jiǎng),帶領(lǐng)團(tuán)隊(duì)多次獲得團(tuán)隊(duì)優(yōu)秀貢獻(xiàn)獎(jiǎng)。著有《智能搜索和推薦系統(tǒng):原理、算法與應(yīng)用》《聊天機(jī)器人:入門、進(jìn)階與實(shí)戰(zhàn)》,其中前者在2022年被某電商平臺(tái)評(píng)為“人工智能領(lǐng)域最受讀者喜愛圖書ToP5”。

圖書目錄

Contents..目  錄
推薦序一
推薦序二
推薦序三
前言
第一篇 圖數(shù)據(jù)與圖模型
第1章 圖數(shù)據(jù)基礎(chǔ) ··························2
1.1 數(shù)學(xué)基礎(chǔ) ·····································2
1.2 圖的基本知識(shí) ······························4
1.2.1.什么是圖 ·························4
1.2.2.圖中基本元素及定義 ·········5
1.3 圖的表示方法 ····························10
1.3.1.圖的代數(shù)表示 ················11
1.3.2.圖的遍歷 ·······················13
1.4 圖數(shù)據(jù)及圖神經(jīng)網(wǎng)絡(luò) ··················14 
1.4.1.圖數(shù)據(jù)的性質(zhì) ················14
1.4.2.圖數(shù)據(jù)應(yīng)用 ···················15
1.4.3.圖神經(jīng)網(wǎng)絡(luò)的發(fā)展史 ·······16
1.5 本章小結(jié) ···································17
第2章 圖神經(jīng)網(wǎng)絡(luò)基礎(chǔ) ·················18
2.1 神經(jīng)網(wǎng)絡(luò)的基本知識(shí) ··················18
2.1.1.神經(jīng)元 ··························19
2.1.2.前饋神經(jīng)網(wǎng)絡(luò) ················22
2.1.3.反向傳播 ·······················23
2.2 卷積神經(jīng)網(wǎng)絡(luò) ····························24
2.2.1.卷積神經(jīng)網(wǎng)絡(luò)基本概念
和特點(diǎn) ··························25
2.2.2.卷積神經(jīng)網(wǎng)絡(luò)模型 ··········29
2.3 循環(huán)神經(jīng)網(wǎng)絡(luò) ····························30
2.3.1.循環(huán)神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)和
特點(diǎn) ·····························31
2.3.2.循環(huán)神經(jīng)網(wǎng)絡(luò)模型 ··········35
2.4 圖神經(jīng)網(wǎng)絡(luò) ································36
2.4.1.圖神經(jīng)網(wǎng)絡(luò)綜述 ·············36
2.4.2.卷積圖神經(jīng)網(wǎng)絡(luò) ·············41
2.4.3.循環(huán)圖神經(jīng)網(wǎng)絡(luò) ·············42
2.5 本章小結(jié) ···································44
第3章 知識(shí)圖譜基礎(chǔ) ·····················46
3.1 知識(shí)圖譜的定義和模型 ···············46
3.1.1.知識(shí)圖譜定義 ················47
3.1.2.知識(shí)圖譜嵌入 ················48
3.1.3.距離變換模型 ················51
3.1.4.語義匹配模型 ················53
3.2 知識(shí)圖譜上的神經(jīng)網(wǎng)絡(luò) ···············55
3.2.1.關(guān)系圖卷積網(wǎng)絡(luò) ·············55
3.2.2.知識(shí)圖譜與注意力模型 ·····55
3.3 本章小結(jié) ···································59
第二篇 推薦系統(tǒng)
第4章 推薦系統(tǒng)架構(gòu) ·····················62
4.1 推薦系統(tǒng)的邏輯架構(gòu) ··················62
4.2 推薦系統(tǒng)的技術(shù)架構(gòu) ··················67
4.3 推薦系統(tǒng)的數(shù)據(jù)和模型部分 ········69
4.3.1.推薦系統(tǒng)中的數(shù)據(jù)平臺(tái)
建設(shè) ·····························69
4.3.2.推薦系統(tǒng)中的數(shù)據(jù)挖掘
方法 ·····························73
4.3.3.推薦系統(tǒng)模型 ················76
4.4 推薦系統(tǒng)的評(píng)估 ·························81
4.4.1.推薦系統(tǒng)的評(píng)估實(shí)驗(yàn)
方法 ·····························89
4.4.2.離線評(píng)估 ·······················89
4.4.3.在線評(píng)估 ·······················92
4.5 基于GNN的推薦系統(tǒng)架構(gòu) ·········94
4.6 本章小結(jié) ···································96
第5章 基于GNN的推薦系統(tǒng)構(gòu)
建基礎(chǔ) ·······························97
5.1 關(guān)于嵌入 ···································97
5.2 Word2Vec ·································102
5.2.1.哈夫曼樹與哈夫曼編

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