Information Retrieval: Algorithms and Heuristics (The Information Retrieval Series)(2nd Edition)

Easily the best introduction to the field of IR. Its hallmarks are:1. very readable (concepts presented in layman terms) and directly applicable (you can literally read and apply the concepts in the real world)2. excellent survey of the field with an comprehensive compedium of references for further reading (surveys, topical and detailed references)3. the only book with latest information on IR strategies and utilities - so far (May 2008)For the learnings you will get out of this book - it is a real BARGAIN. You could easily spend hours and hours of your time just trying to figure out what to read. Its a gem. Its not expensive. Buy it->implement it->realize success from it! buy it now :).To improve your understanding from a novice level to an intermediatre level, would recommend the following books:A. Introduction 1st book: Information Retrieval (this book) David Grossman and Ophir FriederB. Modern Information Retrieval by Ricardo Baeza-Yates (more technical and deeper)C. Managing Gigabytes: Compressing and Indexing Documents and Images by by Ian H. Witten, Alistair Moffat, Timothy C. Bell (best for implementing and a decent overview)D. Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran (algos/pseudo-code, good utilities and concise summaries)F. Mining the web, by Chakrabarti (excellent intro the field of web mining, with some excellent chapters on IR)G. Books by Salton (vector models and fuzzy sets)H. IR book by C. J. Rijsbergen (probabilistic models; available online for free)I. the book by Lesk et. al.J. Follow-up by reading a ton of papers available on citeseer or via googleGood luck!

The Second Edition of "Information Retrieval", by Grossman and Frieder is one of the best books you can find as a introductory guide to the field, being well fit for a undergraduate or graduate course on the topic. It is somewhat a parallel to "Modern Information Retrieval", by Baeza-Yates and Ribeiro-Neto.Chapter 2, "Retrieval Strategies", make a very good review of the main information retrieval models. The main characteristic of this chapter is that it also give some introduction to the theory needed to understand the models. In this chapter, the authors take care to provide not only equations, but also examples of how these equations work in small sets of documents, making it easier for student to grasp their workings. It is a long and detailed chapter, maybe the best you can find among the field.Chapters 3 to 8, make a broad review of IR techniques, however they do not go deep in any technique. The best part is that they have pointers to all the techniques discussed. Those chapters, are actually broad surveys of the title chapter topic.

This is an odd book. Some of what it covers, it covers extremely well and succinctly. However, this book cannot stand alone as your only IR book--there's too many important topics missing. Manning's book is a better initial starting point in terms of breadth. However, given that the subtitle is "Algorithms and Heuristics", I suppose this book isn't attempting to stand alone!

This is a very clear and current book on information storage and retrieval. If you are assigned this book as a textbook in a class, then the book is going to make the task of understanding the material much easier. All of the algorithms are clearly explained and the background material in probability is clearly outlined with good examples and figures. However, I still think I prefer  Modern Information Retrieval  for the theory of information storage and retrieval. It's out of print, but you can easily find it used and just like in this book, all of the background mathematics is outlined in regards to the algorithms and tasks at hand. The other book I'd recommend is  Programming Collective Intelligence: Building Smart Web 2.0 Applications . That book is on the cutting edge of using information retrieval techniques for web applications with plenty of code examples. Even if you go with this book over Modern Information Retrieval as a main source of instruction, Programming Collective Intelligence is a good book about information retrieval in combination with artificial intelligence as it is applied to the web.

Contains a bad mathematical mistake in section 2.2.1 on page 22.The probability P(win|sunny,good-shortstop) cannot be derived from P(win|sunny) and P(win|good-shortstop). It can take any value, even zero.Suppose, shortstop is a vampire and plays good only on cloudy weather, and on sunny weather he always leads his team to defeat. It doesn't contradict to having positive P(win|sunny) and P(win|good-shortstop).

This book provides an excellent blend of theoretical and practical knowledge of the IR field, particularly for those of us with a computer science background, yet no practical working experience in IR. In my opinion, the math is an essential part of expressing the concepts more formally, so it was refreshing to see the authors incorporate just enough formulae, but no more. This book is not going to provide you with a set of recipes for building an indexing or search engine, nor would I expect it do so. However, it does give you an idea of how such engines might be built. Further, I found this book to be a necessary prerequisite for other practitioner-oriented texts, such as  Lucene in Action (In Action series) . Anyone delving into this field for the first time and attempting to use libraries like Lucene may find it difficult to fully exploit its capabilities without a firm understanding of the theoretical underpinnings of IR.

If you're working in the IR industry, or want to develop software in this field, this book is a great starting point. A clarification: this will is not a book for researchers -- instead think of it as a book for advanced practitioners or engineers needing to work in this area. Inside you'll see complete worked examples of several fundamental computations rather than detailed proofs.

Information Retrieval sounds simple at an abstract level, but the various techniques used to match a query with relevant documents contain many little complex details and math formulas that can be daunting. I wasn't sure if one of these techniques could be suitable for a problem in my Ph.D. thesis so I was looking for a book that could cover information retrieval techniques with breadth and depth.I found this book very useful to understand how the various techniques worked, how to apply them, and what were the assumptions behind each techniques. This was very important in determining if a technique was suitable for my problem.What makes this book a good learning material is that it provides small and "digestible" examples for most of the techniques reviewed. The examples are given in a way that the reader can apply the strategies and formulas on example data and see the result. The examples are not exercises, but are integrated with the main text, so the book really walk the reader through them.