Introduction to information retrieval

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1. PheonixPhartsMay 2024
   Once you have vector representations the "similarity" scores are just basic linear algebra. It's fundamentally no different than any other IR/recsys task.

   A good overview is chapter 6 of the Stanford NLP group's IR book [0].

   Engineering LLMs still requires a good foundation in the basics of ML/NLP so it's worth the time to catch up a bit.

   0. https://web.archive.org/web/20231207074155/https://nlp.stanf...

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2. jonstewartDec 2023
   Manning’s Introduction to Information Retrieval is awfully useful. https://nlp.stanford.edu/IR-book/information-retrieval-book....
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3. IKantReadOct 2023
   The classic Information Retrieval, though out of date in many ways, does a great job covering the "old school" NLP approaches for IR and has an excellent section on Latent Semantic Indexing which you might find enjoyable: https://nlp.stanford.edu/IR-book/html/htmledition/latent-sem...
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4. marginalia_nuMay 2023
   If you ever decide to build a search engine, these are good resources.

   Search Engines Information Retrieval in Practice

   Croft, Metzler & Strohman (2015)

   https://ciir.cs.umass.edu/irbook/

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   Introduction to Information Retrieval

   Manning, Raghavan & Schütze (2008)

   https://nlp.stanford.edu/IR-book/html/htmledition/irbook.htm...

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   The Anatomy of a Large-Scale Hypertextual Web Search Engine

   Page & Brin (2000)

   http://infolab.stanford.edu/~backrub/google.html

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   Information Retreival

   Rijsbergen (1979)

   https://www.dcs.gla.ac.uk/Keith/Preface.html

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5. ssnNov 2022
   Three reference textbooks are available openly:

   * Introduction to Information Retrieval, http://informationretrieval.org/

   * Information Retrieval in Practice, http://www.search-engines-book.com/

   * Entity-Oriented Search, https://eos-book.org/

   Modern Information Retrieval is also a classic reference. Not openly available but some contents are (were?) available online. Their site seems to be down but the Internet Archive has a copy.

   Additional resources here:

   * https://nlp.stanford.edu/IR-book/information-retrieval.html http://web.archive.org/web/20220708135205/http://grupoweb.up...

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6. mrazomorAug 2022
   Basic techniques: https://nlp.stanford.edu/IR-book/information-retrieval-book....

   Smartness (NLP): https://web.stanford.edu/~jurafsky/slp3/

   There are also newer techniques, like "deep learning" for search. Not sure what's a good resource for that (using ML to learn the scoring functions is more than a decade old field (learning to rank), it just surfaced again because of the new ML techniques).

   Check the table of contents of those books. Courses which mention chapters or sections from them are probably a good choice.

   You can also check Apache Lucene and do a deep dive to see state-of-the-art implementation (Lucene in Action is a good introduction).

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