本书从计算机科学领域的角度出发，介绍了信息检索的基础知识，并对当前信息检索的发展做了回顾，重点介绍了搜索引擎的核心技术，如文档分类和文档聚类问题，以及机器学习和数值计算方法。书中所有重要的思想都用示例进行了解释，生动形象，引人入胜，实现了理论与实战的完美结合。

本书是信息检索的教材，旨在从计算机科学的视角提供一种现代的信息检索方法。书中从基本概念讲解网络搜索以及文本分类和文本聚类等，对收集、索引和搜索文档系统的设计和实现的方方面面、评估系统的方法、机器学习方法在文本收集中的应用等给出了最新的讲解。

书中所有重要的思想都是用示例进行解释，图文并茂。本书非常适合作为计算机科学及相关专业的高年级本科生和研究生的“信息检索”课程的入门教材，当然也同样适合研究人员和专业人士阅读。

1 Boolean retrieval 1

 1.1 An example information retrieval problem 3

 1.2 A first take at building an inverted index 6

 1.3 Processing Boolean queries 9

 1.4 The extended Boolean model versus ranked retrieval 13

 1.5 References and further reading 16

2 The term vocabulary and postings lists 18

 2.1 Document delineation and character sequence decoding 18

 2.2 Determining the vocabulary of terms 21

 2.3 Faster postings list intersection via skip pointers 33

 2.4 Positional postings and phrase queries 36

 2.5 References and further reading 43

3 Dictionaries and tolerant retrieval 45

 3.1 Search structures for dictionaries 45

 3.2 Wildcard queries 48

 3.3 Spelling correction 52

 3.4 Phonetic correction 58

 3.5 References and further reading 59

4 Index construction 61

 4.1 Hardware basics 62

 4.2 Blocked sort-based indexing 63

 4.3 Single-pass in-memory indexing 66

 4.4 Distributed indexing 68

 4.5 Dynamic indexing 71

 4.6 Other types of indexes 73

 4.7 References and further reading 76

5 Index compression 78

 5.1 Statistical properties of terms in information retrieval 79

 5.2 Dictionary compression 82

 5.3 Postings file compression 87

 5.4 References and further reading 97

6 Scoring, term weighting, and the vector space model 100

 6.1 Parametric and zone indexes 101

 6.2 Term frequency and weighting 107

 6.3 The vector space model for scoring 110

 6.4 Variant tf–idf functions 116

 6.5 References and further reading 122

7 Computing scores in a complete search system 124

 7.1 Efficient scoring and ranking 124

 7.2 Components of an information retrieval system 132

 7.3 Vector space scoring and query operator interaction 136

 7.4 References and further reading 137

8 Evaluation in information retrieval 139

 8.1 Information retrieval system evaluation 140

 8.2 Standard test collections 141

 8.3 Evaluation of unranked retrieval sets 142

 8.4 Evaluation of ranked retrieval results 145

 8.5 Assessing relevance 151

 8.6 A broader perspective: System quality and user utility 154

 8.7 Results snippets 157

 8.8 References and further reading 159

9 Relevance feedback and query expansion 162

 9.1 Relevance feedback and pseudo relevance feedback 163

 9.2 Global methods for query reformulation 173

 9.3 References and further reading 177

10 XML retrieval 178

 10.1 Basic XML concepts 180

 10.2 Challenges in XML retrieval 183

 10.3 A vector space model for XML retrieval 188

 10.4 Evaluation of XML retrieval 192

 10.5 Text-centric versus data-centric XML retrieval 196

 10.6 References and further reading 198

11 Probabilistic information retrieval 201

 11.1 Review of basic probability theory 202

 11.2 The probability ranking principle 203

 11.3 The binary independence model 204

 11.4 An appraisal and some extensions 212

 11.5 References and further reading 216

12 Language models for information retrieval 218

 12.1 Language models 218

 12.2 The query likelihood model 223

 12.3 Language modeling versus other approaches in information retrieval 229

 12.4 Extended language modeling approaches 230

 12.5 References and further reading 232

13 Text classification and Naive Bayes 234

 13.1 The text classification problem 237

 13.2 Naive Bayes text classification 238

 13.3 The Bernoulli model 243

 13.4 Properties of Naive Bayes 245

 13.5 Feature selection 251

 13.6 Evaluation of text classification 258

 13.7 References and further reading 264

14 Vector space classification 266

 14.1 Document representations and measures of relatedness in vector spaces 267

 14.2 Rocchio classification 269

 14.3 k nearest neighbor 273

 14.4 Linear versus nonlinear classifiers 277

 14.5 Classification with more than two classes 281

 14.6 The bias–variance tradeoff 284

 14.7 References and further reading 291

15 Support vector machines and machine learning on documents 293

 15.1 Support vector machines: The linearly separable case 294

 15.2 Extensions to the support vector machine model 300

 15.3 Issues in the classification of text documents 307

 15.4 Machine-learning methods in ad hoc information retrieval 314

 15.5 References and further reading 318

16 Flat clustering 321

 16.1 Clustering in information retrieval 322

 16.2 Problem statement 326

 16.3 Evaluation of clustering 327

 16.4 K-means 331

 16.5 Model-based clustering 338

 16.6 References and further reading 343

17 Hierarchical clustering 346

 17.1 Hierarchical agglomerative clustering 347

 17.2 Single-link and complete-link clustering 350

 17.3 Group-average agglomerative clustering 356

 17.4 Centroid clustering 358

 17.5 Optimality of hierarchical agglomerative clustering 360

 17.6 Divisive clustering 362

 17.7 Cluster labeling 363

 17.8 Implementation notes 365

 17.9 References and further reading 367

18 Matrix decompositions and latent semantic indexing 369

 18.1 Linear algebra review 369

 18.2 Term–document matrices and singular valuede compositions 373

 18.3 Low-rank approximations 376

 18.4 Latent semantic indexing 378

 18.5 References and further reading 383

19 Web search basics 385

 19.1 Background and history 385

 19.2 Web characteristics 387

 19.3 Advertising as the economic model 392

 19.4 The search user experience 395

 19.5 Index size and estimation 396

 19.6 Near-duplicates and shingling 400

 19.7 References and further reading 404

20 Web crawling and indexes 405

 20.1 Overview 405

 20.2 Crawling 406

 20.3 Distributing indexes 415

 20.4 Connectivity servers 416

21 Link analysis 421

 21.1 TheWeb as a graph 422

 21.2 PageRank 424

 21.3 Hubs and authorities 433

 21.4 References and further reading 439

Inde 469

Bibliography 441