Information Retrieval : Advanced Topics and Techniques.

Format:

Book

Author/Creator:

Alonso, Omar.

Contributor:

Baeza-Yates, R. (Ricardo)

Series:

ACM Bks

Language:

English

Subjects (All):

Information retrieval.

Recommender systems (Information filtering).

Physical Description:

1 online resource (836 pages)

Edition:

1st ed.

Place of Publication:

New York : Association for Computing Machinery, 2025.

Summary:

Deep learning and word embeddings have made significant impacts on information retrieval (IR) by adding techniques based in neural networks and language models. This book, written by international academic and industry experts, brings the field up to date with detailed discussions of new approaches and techniques.

Contents:

Intro

Information Retrieval

Contents

Preface

1 Introduction

1.1 Motivation

1.2 A Bit of History

1.3 Responsible Retrieval Systems

1.4 Content Organization

1.4.1 Foundations

1.4.2 Adaptations and Concerns

1.4.3 Verticals

1.5 Intended Audience

I FOUNDATIONS

2 Neural Information Retrieval

2.1 Introduction

2.2 Text Representations for Ranking

2.2.2 LTR Features

2.2.3 Word Embeddings

2.3 Interaction-focused Systems

2.3.1 Convolutional Neural Networks

2.3.2 Pre-trained Language Models

2.3.4 Ranking with Encoder-Decoder Models

2.3.5 Fine-tuning Interaction-focused Systems

2.3.6 Dealing with Long Texts

2.4 Representation-focused Systems

2.4.2 Multiple Representations

2.4.3 Fine-tuning Representation-focused Systems

2.5 Retrieval Architectures and Vector Search

2.5.3 Locality Sensitive Hashing Approaches

2.5.4 Vector Quantization Approaches

2.5.5 Graph Approaches

2.5.6 Optimizations

2.6 Learned Sparse Retrieval

2.6.1 Document Expansion Learning

2.6.2 Impact Score Learning

2.6.3 Sparse Representation Learning

2.7 Retrieval-augmented Generation

2.8 Conclusions

3 Recommender Systems

3.1 Introduction

3.2 The Recommendation Task

3.3 Recommendation Algorithms

3.3.1 Recommendation as a Machine Learning Problem

3.3.2 Characterizing Approaches Based on Their Input Data

3.3.3 Collaborative Filtering

3.3.3.1 Nearest Neighbors

3.3.3.2 Matrix Factorization

3.3.4 Learning to Rank

3.3.5 Neural Recommendation

3.3.6 Content-based and Hybrid Recommender Systems

3.3.6.1 Pure Content-based Systems

3.3.6.2 Hybrid Recommender Systems

3.3.6.3 Collaborative Filtering with Side Information

3.3.7 Discussion

3.4 Evaluation of Recommender Systems

3.4.1 Online Evaluation

3.4.2 Offline Evaluation.

3.4.3 Offline Data

3.4.3.1 Data Splitting

3.4.3.2 Candidate Item Sampling

3.4.4 Recommendation Task and Metrics

3.4.4.1 Rating Prediction

3.4.4.2 Ranking Quality: Recommendation as an IR Task

3.4.4.3 Collection

3.4.4.4 Query and Information Need

3.4.4.5 Relevance

3.4.4.6 Metrics

3.4.5 Beyond Accuracy

3.4.5.1 Novelty

3.4.5.2 Long-tail Novelty

3.4.5.3 Unexpectedness

3.4.5.4 Serendipity

3.4.5.5 Further Notions

3.4.5.6 Diversity

3.4.5.7 Intra-list Dissimilarity

3.4.5.8 Aspect-based Diversity

3.4.5.9 Coverage

3.4.5.10 Enhancing Novelty and Diversity

3.4.5.11 User Perceptions of Diversity and Novelty

3.5 Sequential and Session-based Recommendation

3.5.1 Problem Definition and Terminology

3.5.2 Algorithms for Sequential and Session-based Recommendation

3.5.2.2 Sequence-aware Matrix Factorization

3.5.2.3 Hybrid Approaches

3.5.2.4 Nearest-neighbors and Other Methods

3.5.3 Evaluation of Sequential and Session-based Recommender Systems

3.5.3.1 Offline Evaluation

3.5.3.2 Data Splitting

3.5.3.3 Making the Measurement

3.5.3.4 Cross-Validation

3.5.3.5 User-centric Evaluation

3.5.3.6 Real-world Evaluation

3.5.4 Discussion and Outlook

3.6 Popularity, Bias, and Recurrence in Recommendation

3.6.1 Countering Bias

3.6.2 Understanding Bias

3.6.3 The Feedback Loop

3.7 Impact and Value of Recommender Systems

3.7.1 Understanding the Impact of Recommendations with the Human in the Loop

3.7.2 Consumer and Business Value of Recommender Systems

3.7.2.1 Recommendation as a Multistakeholder Optimization Problem

3.7.2.2 Impact and Value-Oriented Recommender Systems Evaluation

3.8 Conclusions and Challenges

3.8.1 Summary

3.8.2 Further Readings and Future Directions

3.8.2.1 Conversational Recommender Systems.

3.8.2.2 Fairness in Recommender Systems

3.8.2.3 Offline/Online Misalignment in Evaluation

4 Evaluation of IR Systems

4.1 Introduction

4.2 Offline Evaluation

4.2.2 Evaluation Campaigns

4.2.3 Document Corpora and Topics

4.2.4 Pooling

4.2.5 Crowdsourcing

4.2.6 Multi-armed Bandits

4.3 Evaluation Measures

4.3.3 Average Precision

4.3.4 Discounted Cumulated Gain

4.4 Statistical Significance Testing

4.4.1 Basic Intuition about Statistical Significance Testing

4.4.2 ANalysis Of VAriance

4.4.2.2 Assessment of the Model

4.4.2.3 Effect Size

4.4.2.4 Multiple Comparisons

4.4.2.5 Assumptions

4.5 Offline Evaluation with Online Data

4.5.1 Measures Calibrated with Online Data

4.6 Online Evaluation

4.6.1 Description of Online Evaluation

4.6.2 Online Controlled Experiments

4.6.4 Interleaving

4.6.5 Online Measures

Absolute document level metrics

Absolute ranking level metrics

4.7 Measurements

4.7.1 Overview

4.7.2 The Representational Theory of Measurement

4.7.3 Classification of the Scales of Measurement

4.7.3.1 Nominal Scale

4.7.3.2 Ordinal scale

4.7.3.3 Interval Scale

4.7.3.4 Ratio Scale

4.7.4 Admissible Statistical Operations

4.7.5 Statistical Significance Testing

4.7.6 Why the Measurement Theory Matters to IR Evaluation

4.7.6.1 Averaging System Performance

4.7.6.2 Statistical Significance Testing

4.7.6.3 Score Standardization

4.7.6.4 Topic Difficulty

4.7.7 A Formal Theory of IR Evaluation Measures

4.7.7.1 Early Attempts

4.7.7.2 Current Studies

4.7.8 Implications on Statistical Significance Testing

4.7.8.1 Other Studies

4.8 Conclusions and Challenges

4.8.1 Evaluation of Complex Tasks

4.8.2 Reproducibility

4.8.3 Meaningfulness

4.8.4 Large Language Models and Generative AI

5 Query-driven Search Functionality.

5.1 Introduction

5.2 Problem Definitions

5.2.1 Query Auto-completion

5.2.2 Query Suggestion

5.3 A Framework for Search Assist Systems

5.4 Key Factors in Search Assist Functions

5.4.1 Temporal Factors

5.4.2 Contextual Factors

5.4.3 Location Factors

5.4.4 Demographic Factors

5.4.5 Behavioral Factors

5.5 Algorithms

5.6 Datasets

5.7 Evaluation Metrics

5.7.1 Ranking Metrics

5.7.2 User Assist Metrics

5.7.3 Post Usage Metrics

5.8 Historical Notes

5.9 Conclusions and Challenges

6 Knowledge Graphs and Search

6.1 Introduction

What is a knowledge graph

What knowledge graphs are out there

How to search a knowledge graph

Engines and indexing

Combination with text search and federated search

What else is there to know about knowledge graphs

The future of knowledge graphs

6.2 What Is a Knowledge Graph

6.2.1 Our Toy Knowledge Graph

6.2.2 RDF

6.2.3 Our Revised Toy Knowledge Graph

6.2.4 Reification

6.2.5 Other Kinds of Information

6.3 What Knowledge Graphs Are Out There

6.3.1 Wikidata

6.3.2 Freebase

6.3.3 DBpedia

6.3.4 YAGO

6.3.5 UniProt

6.3.6 PubChem

6.3.7 DBLP

6.3.8 OpenStreetMap

6.4 How to Search a Knowledge Graph: Structured Query Languages

6.4.1 SPARQL

6.4.2 Cypher (Neo4j)

6.5 Engines and Indexing

6.5.1 Object Identifiers

6.5.2 Triple Permutations

6.5.3 Query Planning

Distinctness

Multiplicity

Other columns

Case 1:

Case 2:

6.5.4 Further Improvements

6.5.5 Virtuoso

6.5.6 Blazegraph

6.5.7 Neo4j

6.6 How to Search a Knowledge Graph: Assisting the User

6.6.2 Question Answering

Step 1: Find entities from the knowledge graph mentioned in the question

Step 2: Generating candidates

Step 3: Computing feature vectors

Coverage (Cov):

Step 4: Ranking.

6.7 Combination with Text Search and Federated Search

6.7.1 Keyword Search in Literals

6.7.2 Search in an External Text Corpus Linked to a Knowledge Graph

6.7.3 Federated Search

6.7.4 Use Cases of Federated Search

6.8 Conclusions and Challenges

II ADAPTATIONS AND CONCERNS

7 Conversational Search

7.1 Introduction

7.1.1 Defining Conversational Search

7.1.2 Combined Representation of Conversational Search Definitions

7.1.3 Overview

7.2 Searching Through Conversations

7.2.1 Speech User Interfaces

7.2.2 Spoken Dialogue Systems

7.3 Information Seeking Models, Theories, and Properties

7.3.1 Interactive Information Retrieval

7.3.2 Question Answering

7.3.3 Modeling Information Seeking Through Dialogue

7.3.4 Theoretical Frameworks and Properties for Conversational Search

7.4 Fundamental Search Actions in Conversational Search

7.4.1 Query Formulation

7.4.2 Results Presentation and Answer Organization

7.4.3 Query Reformulation and Refinements

7.5 Fundamental Non-search Actions in Conversational Search

7.5.1 Discourse Management

7.5.2 Navigation

7.5.3 Grounding

7.5.4 Visibility of Information-seeking Partner Status

7.6 Implementing and Evaluating Conversational Systems

7.6.1 Evaluation of Conversational Systems

7.6.1.1 Offline Evaluation

7.6.1.2 Online Evaluation

7.6.2 Data for Conversational Systems

7.7 Conclusions and Challenges

8 Cross-language Retrieval

8.1 Introduction

8.1.1 Some Use Cases

8.1.2 The Three Waves of Information Retrieval

8.2 The Core Technology of CLIR

8.2.1 What to Translate?

8.2.1.1 The Query or the Documents?

8.2.1.2 Which Terms?

8.2.2 Which Translations Are Possible?

8.2.3 How to Use those Translations?

8.3 Updating Nie: CLIR since 2010

8.3.1 What to Translate: Parts of Words.

8.3.2 Which Translations: Bilingual and Multilingual Embeddings.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Other Format:

Print version: Alonso, Omar Information Retrieval

ISBN:

9798400710520

OCLC:

1492916709