Modern information retrieval: the concepts and technology behind search

By Ricardo A. Baeza-Yates, B. Ribeiro-Neto. ChoiceReviews, 2011

913 citations recorded by the source corpus.

Contents Preface Acknowledgements 1 Introduction 2 User Interfaces for Search by Marti Hearst 3 Modeling 4 Retrieval Evaluation 5 Relevance Feedback and Query Expansion 6 Documents: Languages & Properties with Gonzalo Navarro and Nivio Ziviani 7 Queries: Languages & Properties with Gonzalo Navarro 8 Text Classification with Marcos Gonccalves 9 Indexing and Searching with Gonzalo Navarro 10 Parallel and Distributed IR with Eric Brown 11 Web Retrieval with Yoelle Maarek 12 Web Crawling with Carlos Castillo 13 Structured Text Retrieval with Mounia Lalmas 14 Multimedia Information Retrieval by Dulce Poncele'on and Malcolm Slaney 15 Enterprise Search by David Hawking 16 Library Systems by Edie Rasmussen 17 Digital Libraries by Marcos Gonccalves A Open Source Search Engines with Christian Middleton B Biographies Bibliography Index

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/f54bf5c3e2dfbf06612c7d949fcc94e0c0fa842d

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Citations in the corpus

Multi-Domain Neural Machine Translation through Unsupervised Adaptation (2017, methodology): “However, since the similarity measure used in Lucene is based on tf-idf counts (Baeza-Yates and Ribeiro-Neto, 2011), it does not consider the order of the words and n-grams in the query and in the retrieved sentences, which is an important aspect for MT model training.”
A Survey of Techniques for Event Detection in Twitter (2015, background): “…and data mining (Murphy 2012; Hastie et al. 2009), natural language processing (Manning and Schütze 1999; Jurafsky and Martin 2009), information extraction (Hogenboom et al. 2011), text mining (Hogenboom et al. 2011; Aggarwal 2011), and information retrieval (Baeza-Yates and Ribeiro-Neto 2011).”
A Survey of Text Clustering Algorithms (2012, background): “The sparse and high dimensional representation of the different documents necessitate the design of text-specific algorithms for document representation and processing, a topic heavily studied in the information retrieval literature where many techniques have been proposed to optimize document repre”
Dense Text Retrieval Based on Pretrained Language Models: A Survey (2022, background): “Interested readers can refer to [25], [26], [271] for detailed discussions.”
Linked open data to support content-based recommender systems (2012, methodology): “We want to stress here that, among the various algorithms to compute similarities, such as Support Vector Machines and other classifiers [2], we selected the VSM in order to have a good baseline for further developments and comparisons.”
Semantic Matching in Search (2014, background): “See [131, 52, 6] for introduction to the fundamentals of web search system.”
Embedding-based Retrieval in Facebook Search (2020, methodology): “Since it is difficult to accurately compute the search intent from query text and represent the semantic meaning of documents, search techniques are mostly based on various term matching methods [1], which performs well for the cases that keyword match can address.”
Toward Predicting the Outcome of an A/B Experiment for Search Relevance (2015, methodology): “The standard way is to run an A/B experiment ( e.g. , [1, 16]) to directly measure online metrics, or to run an interleaving experiment that gives directional evaluation results [6, 24].”
Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study (2015, background): “The standard approach to evaluating ranking quality of a search engine is to evaluate its ranking results on a set of human-labeled examples and compute relevance metrics like mean average precision [1] and normalized discounted cumulative gain (NDCG) [15].”
From Semantic Retrieval to Conversational Agent: A Web-Based RAG Architecture for Interactive System Dynamics Modeling (2026, unknown): “Retrieval performance was assessed across the 37 benchmark scenarios and six retrieval methodologies using standard IR metrics, as established in the foundational literature by Baeza-Yates and Ribeiro-Neto [38].”