Keyword stuffing is considered to be an unethical Search engine optimization (SEO) technique. Keyword stuffing occurs when a web page is loaded with keywords in the meta tags or in content. The repetition of words in meta tags, may explain why many search engines no longer use these tags.

Keyword stuffing is used is to obtain maximum search engine ranking and visibility for particular phrases. A word that is repeated too often may raise a red flag to search engines.

Hiding text out of view of the visitor is done in many different ways. Text colored to blend with the background, CSS “Z” positioning to place text “behind” an image – and therefore out of view of the visitor – and CSS absolute positioning to have the text positioned several feet away from the page center, are all common techniques. As of 2005, some of these invisible text techniques can be detected by major search engines.

“Noscript” tags are another way to place hidden content within a page. While they are a valid optimization method for displaying an alternative representation of scripted content, they may be abused, since search engines may index content that is invisible to most visitors.

Inserted text sometimes includes words that are frequently searched (such as “sex”) even if those terms bear little connection to the content of a page, in order to attract traffic to advert-driven pages.

Links

• Google Guidelines
• Yahoo! Guidelines

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

Keyword density is the percentage of words on a web page that match a specified set of keywords. In the context of search engine optimization keyword density can be used as a factor in determining whether a web page is relevant to a specified keyword or keyword phrase. Due to the ease of managing keyword density, search engines usually implement other measures of relevancy to prevent unscrupulous webmasters from creating search spam through practices such as keyword stuffing.

There is also much confusion between the notions of similarity and relevance. These are not the same thing. It has often been said by many companies doing topic clustering, document filtering, and other such applications that their algorithms function by grouping relevant documents together. What is actually meant is that the algorithms are grouping similar documents together. Two (or more) documents are never relevant to each other. They may be similar to each other, but they are only ever relevant to a user’s information need. If there is no user information need, there is no relevance.

The cluster hypothesis in information retrieval says that two documents that are similar to each other have a high likelihood of being relevant to the same information need. Documents by themselves, however, are never relevant to each other. Relevance is defined in terms of a user’s information need.

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

In the simplest case, relevance can be calculated by examining how many times a query term appears in a document (term frequency), possibly combined with how discriminative that query term is across the searched collection (often called Term Frequency-Inverse Document Frequency).

Since search engines and other businesses rely upon the accuracy of their results, many additional, more complex algorithms have been developed to estimate result relevance. Many of these algorithms, particularly those used by search engines, are hidden to the public, as a user that knows the details of a search algorithm can artificially boost his own content’s ranking.

Relevance calculation is often misinterpreted by the press. For example, it has often been said that when Google burst onto the scene it was miles ahead of its competitors because it, unlike anyone else, ranked web pages by relevance. This is not true since everyone ranks by relevance. It is just that Google had come up with a fairly new way of estimating relevance, namely PageRank. But even search engines that only use TFIDF rank by relevance.

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

In computer science, and particularly in search engines, relevance is a numerical score assigned to a search result, representing how well the result meets the information need of the user that issued the search query. In many cases, a result’s relevance determines the order in which it is presented to the user.

In academic information retrieval, the word relevance has been used in system evaluation for over forty years, going back to the Cranfield Experiments of the early 1960s. In the relatively new commercial search realm, among web search engine companies, search engine optimizers, and in the press, the incorrect relevancy is mistakenly being used more and more instead of the correct relevance. One can often tell from which community an information retrieval practitioner hails, depending on whether he or she uses the correct form of the word. Wikipedia’s search facility is an example of use of the incorrect relevancy.

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.