Search Engines: Information Retrieval in Practice

By W. Bruce Croft, Donald Metzler, Trevor Strohman. 2009

1,227 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Retrieval & Search

Citations in the corpus

End-to-End Neural Ad-hoc Ranking with Kernel Pooling (2017, background): “Retrieval models such as query likelihood and BM25 are based on exact matching of query and document words, which limits the information available to the ranking model and may lead to problems such vocabulary mismatch [4].”
Relevance-based Word Embedding (2017, methodology): “Relevance feedback has been shown to be highly eective in improving retrieval performance [7, 32].”
Boosting Search Engines with Interactive Agents (2021, neutral): “Croft et al. (2009, §6.2) estimate that less than 0.5% use ‘+’.”
Context-Aware Document Term Weighting for Ad-Hoc Search (2020, background): “Finally, HDCT stores BoWHDCT, the document bag-of-words representation, into an inverted index, where the new context-aware term weights replace the standard term frequency fields in the inverted lists [7].”
Deep Learning for Matching in Search and Recommendation (2018, background): “Those who are not familiar with the areas may consult existing materials (e.g., Adomavicius and Tuzhilin, 2005; Croft et al., 2009; Li and Xu, 2014; Liu, 2009; Ricci et al., 2015).”
A survey on question answering technology from an information retrieval perspective (2011, background): “They have the advantage of low analysis complexity and low storage overhead for the indices, which are often in the form of term posting lists, so called inverted files [25].”
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing (2018, positive): “All retrieval experimentswere carried out using theGalago search engine [10].”
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, neutral): “The F -measure is a harmonic mean between precision p and recall r [44].”
Deep Reinforced Query Reformulation for Information Retrieval (2020, background): “rocess, where the original query is reformulated (typically expanded) using terms occurring in the pseudo-relevant set of documents–typically the top-ranked documents in response to the initial query [10]. More recently, there has been a move towards addressing more complex information needs whereth”
Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “Finally, a number of general information retrieval textbooks have been recently published [101, 42, 30].”
A Short Introduction to Learning to Rank (2011, neutral): “The probability models can be calculated with the words appearing in the query and document, and thus no training is needed (only tuning of a small number of parameters is necessary) [3].”
Beyond Trending Topics: Real-World Event Identification on Twitter (2011, methodology): “An alternative metric that is sensitive to the rank of the events in the top selected clusters is the normalized discounted cumulative gain (NDCG) metric.”
Selective Weak Supervision for Neural Information Retrieval (2020, methodology): “relevance matches between query and documents [3], we propose ReInfoSelect, “Reinforcement Information retrieval weak supervision Selector”, which conducts selective weak supervision training specifically designed for Neu-IR models.”