这是本全英文版本的信息检索知识读本。主要介绍了信息检索(IR)中的11个关键问题以及其如何影响搜索引擎的设计与实现，并且用数学模型强化了重要的概念。本书内容丰富，针对性、实用性较强，适合作为高等院校计算机科学或计算机工程专业的本科生、研究生的教材使用。

本书介绍了信息检索(IR)中的关键问题，以及这些问题如何影响搜索引擎的设计与实现，并且用数学模型强化了重要的概念。对于网络搜索引擎这一重要的话题，书中主要涵盖了在网络上广泛使用的搜索技术。

本书适用于高等院校计算机科学或计算机工程专业的本科生、研究生，对于专业人士而言，本书也不失为一本理想的入门教材。

1 Search Engines and Information Retrieval

　1.1 What Is Information Retrieval？

　1.2 The Big Issues

　1.3 Search Engines

　1.4 Search Engineers

2 Architecture of a Search Engine

　2.1 What Is an Architecture？

　2.2 Basic Building Blocks

　2.3 Breaking It Down

　　2.3.1 Text Acquisition

　　2.3.2 Text Transformation

　　2.3.3 Index Creation

　　2.3.4 User Interaction

　　2.3.5 Ranking

　　2.3.6 Evaluation

　2.4 How Does It Really Work？

3 Crawls and Feeds

　3.1 Deciding What to Search

　3.2 Crawling the Web

　　3.2.1 Retrieving Web Pages

　　3.2.2 The Web Crawler

　　3.2.3 Freshness

　　3.2.4 Focused Crawling

　　3.2.5 Deep Web

　 3.2.6 Sitemaps

　 3.2.7 Distributed Crawling

 3.3 Crawling Documents and Email

 3.4 Document Feeds

 3.5 The Conversion Problem

　 3.5.1 Character Encodings

 3.6 Storing the Documents

　 3.6.1 Using a Database System

　 3.6.2 Random Access

　 3.6.3 Compression and Large Files

　 3.6.4 Update

　 3.6.5 BigTable

　3.7 Detecting Duplicates

　3.8 Removing Noise

4 Processing Text

　4.1 From Words to Terms

　4.2 Text Statistics

　 4.2.1 Vocabulary Growth

　 4.2.2 Estimating Collection and Result Set Sizes

 4.3 Document Parsing

　 4.3.1 Overview

　 4.3.2 Tokenizing

　 4.3.3 Stopping

　 4.3.4 Stemming

　 4.3.5 Phrases and N-grams

 4.4 Document Structure and Markup

 4.5 Link Analysis

　 4.5.1 Anchor Text

　 4.5.2 PageRank

　 4.5.3 Link Quality

 4.6 Information Extraction

　 4.6.1 Hidden Markov Models for Extraction

 4.7 Internationalization

5 Ranking with Indexes

 5.1 Overview

 5.2 Abstract Model of Ranking

 5.3 Inverted Indexes

5.3.1 Documents

5.3.2 Counts

5.3.3 Positions

5.3.4 Fields and Extents

5.3.5 Scores

5.3.6 Ordering

 5.4 Compression

5.4.1 Entropy and Ambiguity

5.4.2 Delta Encoding

5.4.3 Bit-Aligned Codes

5.4.4 Byte-Aligned Codes

5.4.5 Compression in Practice

5.4.6 Looking Ahead

5.4.7 Skipping and Skip Pointers

 5.5 Auxiliary Structures

 5.6 Index Construction

5.6.1 Simple Construction

5.6.2 Merging

5.6.3 Parallelism and Distribution

5.6.4 Update

 5.7 Query Processing

5.7.1 Document-at-a-time Evaluation

5.7.2 Term-at-a-time Evaluation

5.7.3 Optimization Techniques

5.7.4 Structured Queries

5.7.5 Distributed Evaluation

5.7.6 Caching

6 Queries and Interfaces

 6.1 Information Needs and Queries

 6.2 Query Transformation and Refinement

6.2.1 Stopping and Stemming Revisited

6.2.2 Spell Checking and Suggestions

6.2.3 Query Expansion

6.2.4 Relevance Feedback

6.2.5 Context and Personalization

 6.3 Showing the Results

6.3.1 Result Pages and Snippets

6.3.2 Advertising and Search

6.3.3 Clustering the Results

 6.4 Cross-Language Search

7 Retrieval Models

 7.1 Overview of Retrieval Models

7.1.1 Boolean Retrieval

7.1.2 The Vector Space Model

 7.2 Probabilistic Models

7.2.1 Information Retrieval as Classification

7.2.2 The BM25 Ranking Algorithm

 7.3 Ranking Based on Language Models

7.3.1 Query Likelihood Ranking

7.3.2 Relevance Models and Pseudo-Relevance Feedback

 7.4 Complex Queries and Combining Evidence

7.4.1 The Inference Network Model

7.4.2 The Galago Query Language

 7.5 Web Search

 7.6 Machine Learning and Information Retrieval

7.6.1 Learningto Rank

7.6.2 Topic Models and Vocabulary Mismatch

 7.7 Application-Based Models

8 Evaluating Search Engines

 8.1 Why Evaluate ？

 8.2 The Evaluation Corpus

 8.3 Logging

 8.4 Effectiveness Metrics

8.4.1 Recall and Precision

8.4.2 Averaging and Interpolation

8.4.3 Focusing on the Top Documents

8.4.4 Using Preferences

  8.5 Efficiency Metrics

  8.6 Training, Testing, and Statistics

8.6.1 Significance Tests

8.6.2 Setting Parameter Values

8.6.3 Online Testing

 8.7 The Bottom Line

9 Classification and Clustering

 9.1 Classification and Categorization

9.1.1 Naive Bayes

9.1.2 Support Vector Machines

9.1.3 Evaluation

9.1.4 Classifier and Feature Selection

9.1.5 Spam, Sentiment, and Online Advertising

 9.2 Clustering

9.2.1 Hierarchical and K-Means Clustering

9.2.2 K Nearest Neighbor Clustering

9.2.3 Evaluation

9.2.4 How to Choose K

9.2.5 Clustering and Search

10 Social Search

 10.1 What Is Social Search？

 10.2 User Tags and Manual Indexing

10.2.1 Searching Tags

10.2.2 Inferring Missing Tags

10.2.3 Browsing and Tag Clouds

 10.3 Searching with Communities

10.3.1 What Is a Community？

10.3.2 Finding Communities

10.3.3 Community-Based Question Answering

10.3.4 Collaborative Searching

 10.4 Filtering and Recommending

10.4.1 Document Filtering

10.4.2 Collaborative Filtering

 10.5 Peer-to-Peer and Metasearch

10.5.1 Distributed Search

10.5.2 P2P Networks

11 Beyond Bag of Words

  11.1 Overview

  11.2 Feature-Based Retrieval Models

  11.3 Term Dependence Models

  11.4 Structure Revisited

11.4.1 XML Retrieval

11.4.2 Entity Search

  11.5 Longer Questions, Better Answers

  11.6 Words, Pictures, and Music

  11.7 One Search Fits All？

References

Index