{"id":15722,"date":"2022-10-17T13:49:50","date_gmt":"2022-10-17T13:49:50","guid":{"rendered":"https:\/\/www.searchenginejournal.com\/information-retrieval-seo\/464164\/"},"modified":"2022-10-17T13:49:50","modified_gmt":"2022-10-17T13:49:50","slug":"information-retrieval-an-introduction-for-seos-via-sejournal-taylordanrw","status":"publish","type":"post","link":"https:\/\/marketingnewsbox.com\/?p=15722","title":{"rendered":"Information Retrieval: An Introduction For SEOs via @sejournal, @TaylorDanRW"},"content":{"rendered":"
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When we talk about information retrieval, as SEO pros, we tend to focus heavily on the information collection stage \u2013 the crawling.<\/p>\n

During this phase, a search engine would discover and crawl URLs that it has access to (the volume and breadth depending on other factors we colloquially refer to as a crawl budget<\/a>).<\/p>\n

The crawl phase isn\u2019t something we\u2019re going to focus on in this article, nor am I going to go in-depth on how indexing works.<\/p>\n

If you want to read more on crawl and indexing, you can do so here<\/a>.<\/p>\n

In this article, I will cover some of the basics of information retrieval, which, when understood, could help you better optimize web pages for ranking performance.<\/p>\n

It can also help you better analyze algorithm changes<\/a> and search engine results page (SERP) updates.<\/p>\n

To understand and appreciate how modern-day search engines process practical information retrieval, we need to understand the history of information retrieval on the internet \u2013 particularly how it relates to search engine processes.<\/p>\n

Regarding digital information retrieval and the foundation technologies adopted by search engines, we can go back to the 1960s and Cornell University, where Gerard Salton led a team that developed the SMART Information Retrieval System.<\/p>\n

Salton is credited with developing and using vector space modeling for information retrieval.<\/p>\n

Vector Space Models<\/h2>\n

Vector space models<\/a> are accepted in the data science community<\/a> as a key mechanism in how search engines \u201csearch\u201d and platforms such as Amazon provide recommendations.<\/p>\n

This method allows a processor, such as Google, to compare different documents with queries when queries are represented as vectors.<\/p>\n

Google has referred to this in its documents as vector similarity search, or \u201cnearest neighbor search,\u201d defined by Donald Knuth in 1973.<\/p>\n

In a traditional keyword search, the processor would use keywords, tags, labels, etc., within the database to find relevant content.<\/p>\n

This is quite limited, as it narrows the search field within the database because the answer is a binary yes or no. This method can also be limited when processing synonyms<\/a> and related entities.<\/p>\n

The closer the two entities are in terms of proximity, the less space between the vectors, and the higher in similarity\/accuracy they are deemed to be.<\/p>\n

To combat this and provide results for queries with multiple common interpretations, Google uses vector similarity to tie various meanings, synonyms, and entities together.<\/p>\n

A good example of this is when you Google my name.<\/p>\n

To Google, [dan taylor] can be:<\/p>\n