Understanding Search Engines: Mathematical Modeling and Text Retrieval (Software, Environments, Tools) (Paperback)

~ (Author), Murray Browne (Author) "We expect a lot from our search engines..." (more)
Key Phrases: Bread Search, Child Home Safety, Symbol Name Formula

Editorial Reviews

Review

"...Any search engine user ought to dip into this accessible book for a reality check. Highly recommended." -- CHOICE, Vol. 38, No. 2, October 2000.

"...It has been used so often that it's barely legible from all the coffee stains!" -- Art Blevins, eCollege.com

"...The book is easy to read and is a good introduction on how search engines operate..." -- Christopher Dean, Mathematics Today, October 1999.

"Here is a small, compact book, written in a refreshingly informal manner, about the techniques of text searching.…" -- Ian H. Witten, University of Waikato, SIAM Review, Vol. 42, No. 3, September 2000.

Review

‘There is no other information retrieval/search book where the heart is the mathematical foundations. This book is greatly needed to further establish information retrieval as a serious academic, as well as practical and industrial, area.’ Jaime Carbonell, Carnegie Mellon University

‘Berry and Browne describe most of what you need to know to design your own search engine. Their strength is the description of the solid mathematical underpinnings at a level that is understandable to competent engineering undergraduates, perhaps with a bit of instructor guidance. They discuss the algorithms used by most commercial search engines, so you may find your use of Google and its kind becomes more effective, too.’ George Corliss, Marquette University.

‘This book gives a valuable, generally non-technical, insight into how search engines work, how to improve the users' success in Information Retrieval (IR), and an in-depth analysis of a mathematical algorithm for improving a search engine's performance. …Written in an informal style, the book is easy to read and is a good introduction on how search engines operate…’ Christopher Dean, Mathematics Today

‘Anyone interested in building their own search engine, or looking for a compact and readable introduction to the field of modern information retrieval will find this book to be an excellent first introduction.’ Tony Donaldson, MAA Reviews --This text refers to an alternate Paperback edition.

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Product Details

• Paperback: 116 pages
• Publisher: Soc for Industrial & Applied Math (June 1999)
• Language: English
• ISBN-10: 0898714370
• ISBN-13: 978-0898714371
• Product Dimensions: 9 x 6 x 0.4 inches
• Shipping Weight: 4.2 ounces
• Average Customer Review: 3.1 out of 5 stars  See all reviews (7 customer reviews)
• Amazon.com Sales Rank: #1,578,851 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

26 of 30 people found the following review helpful:

4.0 out of 5 stars Appreciations from a novice., June 29, 2000

By A Customer

I read this book because I am starting academic research on search engines. It was one of my first books on the subject. It actually deals with two aspects: (1) relevant issues in search engine design, and (2) a mathematically sound approach in building and querying large index strucutres. The explanation in the book on both aspects is short but to the point and explained in an understandable way. It also contains a short list + description of some key references. Great !

7 of 7 people found the following review helpful:

4.0 out of 5 stars Good Introduction, May 7, 2007

By	Geraldo XEXEO "Computer Geek and Professor" (Rio de Janeiro, Brazil) - See all my reviews (REAL NAME)

Amazon Verified Purchase

This review is from: Understanding Search Engines: Mathematical Modeling and Text Retrieval (Software, Environments, Tools), Second Edition (Paperback)

There are better books in the market, and even the author would be the first to recognize it. However, this book is one of the most clear and readable introduction to the subject that you can find.

The author fully acomplishes the objective: teach his reader, at undergratuate level, how search engines work. Even some difficult subject, such as LSI, are treated at a level one can easilly understand.

One of the most important characteristics of the book is that it does math. Every formula has an example, usually using small matrix that allow the reader to easilly follow them.

The book is suitable for an objective introduction to the field. It is not very "academic", in the sense it is rather informal. If it is not a textbook, it could help some bewildered student to grasp the inner workings. It could also help a teacher to find clearer ways for explanations and good examples for classroom.

1 of 1 people found the following review helpful:

5.0 out of 5 stars Linear Algebra, Numerical Linear Algebra, and Search Engine, January 19, 2009

By	Man Kam Tam (Calexico, CA USA) - See all my reviews (REAL NAME)

This review is from: Understanding Search Engines: Mathematical Modeling and Text Retrieval (Software, Environments, Tools), Second Edition (Paperback)

Other than showing the readers how to design a search engine, the authors, Michael W. Berry and Murray Browne of "Understanding Search Engines: Mathematical Modeling, and Text Retrieval," intend to fill the gap between applied mathematics and information management. In a latent semantic index (LSI) system, mathematics plays a major role in search engine performance. The term-by-document matrix of the system would be transformed to a lower rank matrix for conceptual indexing. However, nobody knows how low the rank should be for the best performance. The best technique so far for lower rank approximation is called singular value decomposition. In such a system, vectors model both documents and queries. The angle between the document vector and the query vector determines the rank-order of the document. The elements of the vectors are usually the weighted frequency of the term occurrence. Thus the searchers should list as many terms as possible in their queries for better search results.

LSI search engine is good for small document system only. Other searching methods such as HITS and PageRank are introduced. For the readers who have the background on linear algebra, numerical linear algebra, and search engine should find this book interesting.

Generally speaking, the book is brief. It has 117 pages and 9 chapters. The nine chapters are Introduction, Document File Preparation, Vector Space Models, Matrix Decompositions, Query Management, Ranking and Relevance Feedback, Searching by Link Structure, User Interface Considerations, and Further Reading. Chapter two (Document File Preparation) reminds the readers that the documents of the system needed to be "clean-up" and index. The works may require plenty of manual labor.