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“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, October 1999. The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Readers will find that the second edition includes significant changes that bring the text up to date on current information retrieval methods. For example, the authors have added a completely new chapter on link-structure algorithms used in search engines such as Google, and the chapter on user interface has been rewritten to specifically focus on search engine usability. To reflect updates in the literature on information retrieval, the authors have added new recommendations for further reading and expanded the bibliography. In addition, the index has been updated and streamlined to make it more reader friendly. Instructors will find that the book serves as an excellent companion text for courses in information retrieval, applied linear algebra, and scientific computing. Because of the authors’ informal, conversational tone, readers with nonmathematical backgrounds also will appreciate the less technical chapters of the text.
Understanding Search EnginesReviewed by Daniel Pawelec, 2009-02-04
This book is condensed knowladge about the topic of search engines
- but only the basics. I am very dissapointed because I thought I
will see something special, inspiring.
This book is good for beginners in search engines field but not for
the money it costs now.
A mix of good and badReviewed by Ray, 2009-01-21
As others have pointed out, this book is very short. As a
consequence, it leaves out a lot of details and forces the reader
to refer to another book. This is more noticeable in the sections
that do not relate to linear algebra (stemming, performance
evaluation, and user interface design). If you want more
information about these topics, it is best to look for another
book.
However, the discussions about latent semantic indexing and
querying based on link structure are more detailed in comparison
and both topics are mentioned within the context of linear
algebra.
Don't expect an introduction to QR or SVD matrix decompositions or
what an eigenspace is. Also, don't expect a proper definition of
what a graph is. For all of this, you will also have to refer to
another book. If you do not need such an introduction, then you may
not mind.
Overall, the book attempts to do too many topics in few pages and
suffers from this. However, if you are looking for a "crash course
in search engines"-type book, then this might be the one for you.
You may end up buying another book afterwards if you want to know
implementation details, though.
Linear Algebra, Numerical Linear Algebra, and Search EngineReviewed by Man Kam Tam, 2009-01-20
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.
Good introductionReviewed by Pat Choi, 2008-11-11
Good first (and short) book on the subject. Easy to follow and understand. Most suitable for reader who has some exposures to numerical analysis and/or numerical linear algebra because QR factorization, SVD, Semidiscrete decomposition (SDD) etc. methods are used in the text.
Good IntroductionReviewed by Geraldo XEXEO, 2007-05-07
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.
