Statistics in the context of Search Engine Optimization (SEO)? I did my undergrad degree in math and am working on a masters in statistics, and have little to no background in SEO. I have a decent background in master's-level statistics (i.e., linear models and Casella and Berger's text).
I was wondering if there are any texts focusing on statistics and/or machine learning with a Search Engine Optimization (SEO) emphasis. There are many, many books out there on SEO, but they tend to be very non-technical and seem to be more written at a high-level perspective. I am not interested in books on the various tools (e.g., Google Analytics) that are out there; I am interested in books that focus on statistical and machine learning techniques for SEO.
Alternatively, I could learn all about time series, machine learning (which I'm currently doing), causality, and accumulate domain knowledge on top of that, but if there's something that currently exists out there which covers this material in the context of SEO... that would be excellent.
 A: First, I would like to explain the history of SEOs and consequently why many SEO books do not provide the exact implementation details for a general audience. 
From my understanding, there are very few people who know exactly how search engines work. If someone knows how Google's search works in great detail, he/she can make a lot of money by discerning how to cheat the search, and (for a profit) ranking someone's website into the top search results. This is why Google/Bing's searches are proprietary rather than open access. 
In addition, the search engine is necessarily a very complex system. You can expect any decent search tool to make use of nested algorithms, thousands of hand written rules, AI-assisted human filtering on content, and so on.
In summary, while components of the system may make use of well known learning procedures, the overall system cannot be described simply by math. Even if that were possible, the system is sure to reflect newer and more sophisticated features by the time any one person came to grips with its current state. We do not know how often Google update their algorithm, and what exactly those updates entail.
Since no one person knows exactly how things work, some observers conduct experiments to see or modify results. Some tricks are really hacks. For example, some site designers include "dirty / hot / unrelated" key words in the page, but make the text transparent so browsers clicking-through do not see the text although that text essentially swayed the search that originally sought such content. These types of tricks are falling out of favor, and are examples of how Google's dynamic algorithm development quickly matches and overcomes the site designers' biasing efforts.
These are examples of SEO tricks, and illustrates why search implementation quickly deviates from a purely mathematical or statistical treatment.

Above are real world examples of SEO, and why there are not too many math in the SEO books. But if you want to study recommendation system in general, there are many places to start with. Such as 
Learn to rank
Non-negative matrix factorization
Eigendecomposition (Markov process, stationary distribution / original page rank algorithm)
And many more.
