Introduction to Information Retrieval

The company I was working for started using Elastic search (which is built on top of Lucene), so I had to dive into details of Lucene pretty deeply. Since I had no prior background in Information Retrieval field, I decided to learn the theory first and picked up this book for that purpose. This book is a nice introductory text on Information Retrieval covering a lot of ground from index construction including posting lists, tolerant retrieval, different types of queries (boolean, phrase etc), scoring, evalution of information retrieval systems, feedback mechanisms, classifcations, clustering and crawling. Overall I liked the authors presentation style in this book. The concepts are presented very clearly for the most part. With the exception of a few chapters, it's not too math heavy, so it's suited for a wider audience from that perpsective. Web crawling chapters although small are really good. This book is written such that each chapter can be covered in one lecture, so it's nice from instructor's stand point as well. This book is the text used in some schools for Information Retrieval class. You actually don't have to buy this book since it's available online for free (although the page numbers don't match exactly, so if you are taking a class and instructor refers to a certain page, it could be a different page number on the online version). I only skipped a few chapters (Chapter 18 Latent Semantic Indexing for example) but otherwise read the book from cover to cover. It took me two months to read this book but it was well worth it. When I was done, I felt like I had a good understanding of foundations of Information Retrieval field. Since then I looked into Lucene details (using Lucene in Action) and it not only made a lot more sense but actually more enjoyable. Highly recommended without any reservation.

I have no desire to build an internet search engine, so I'm not the target audience. However, I do work with large corpora, some of which are unindexed. When one search I programmed (in R) took 14 hours to complete (this after one attempt produced unusable results due to a bug and another crashed twelve hours in due to the power saver mode kicking in), I knew I had to find a better way.I knew from the free sample that this book was what I was looking for. Thinking this would be a completely a new field to me, I was surprised how much I already knew. Some of it is not relevant to corpus linguists (result ranking for example), but if you're a corpus linguist and want to build an index for your corpus, I doubt you'll find a better book than this.And the Kindle edition is done well, which is not always the case. Websites are hyperlinked and you can jump to the next or previous section with the 5-way controller.

if you want to have a basic understanding about search engine, this book will be the best choice. however, if you hope to do research in this area, this book is not enough.

To understand the brains behind Semantic Web, and Google's today, the book carries a fundamental importance.

Managing Gigabytes used to be my favorite book on search, but it is getting quite dated as this point. This new book is by three search gurus, Chris Manning, Prabhakar Raghavan (head of Yahoo Research), and Hinrich Schutze, and the depth of their expertise shows.This book not only describes how to build a search engine (including crawling, indexing, ranking, classification, and clustering), but also has many of the insights you can only get from lengthy experience using these techniques at large scale.Definitely my new favorite book on search. If you work in search or just have an interest in the field, it is a great read.

The product was not used at all and highly recommend people buying a used textbook so it cheaper.

I purchased this for a class, but it is a nice overview and beginning reference.

Very clean and written so that you can grasp the material if you take your time. Unlike other books, it doesn't just throw a plethora of equations at you and leave you to fend for yourself. Instead, it explains things from a high level (like what machine learning algorithm to use and when) and in detail (what each component of an equation does).

Fico me perguntando: aprende-se com erros? Claro que sim. Por que esse livro propõe exercícios, mas não oferece nenhum gabarito?Ademais, parece haver erros de revisão. Inclusive, tem exercícios que parecem incompletos, ou modificados de última hora, perdendo um pouco do sentido (e o gabarito teria sido muito importante nesses!)Enfim, o material do livro é detalhado - mas estou me convencendo, conforme estudo, que há erros de revisão atrapalhando um bocado a parte teórica também... :-(

Am on chapter 2 and have not seen any print errors so far. The contents of the book are amazing and the author explains the concepts very well.

Buon libro per lo studio

Every topic is well explained

Conform to the description. I used the book to teach Information retrieval. It's the reference book for theoretical Search engine and Information retrievalThanks