Content Recommendations

Content Recommendations anonymously tracks visitorSomeone who visits a website using a web browser. In most cases, a visitor can use public functions and services but cannot create content and has limited access to community content. In an SEO context, visitor means the number of visits to a URL through channels (external referrers), direct arrivals, and internal links. (See also visitor groups.) activity on a website to build a profiles for each visitor. Analysis of a unique profile lets you deliver the most relevant content to each visitor.

For example, based on the visitor's activity on the website, the topic cloud in the following image shows a visitor's interest in retirement, which you can use to recommend articles, blog posts, or other content related to retirement planning.

Image: Interest profile topic cloud

Content Recommendations uses Natural Language Processing (NLPNatural language processing. A subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. - Source: Wikipedia) to match content that shares the greatest degree of similarity of an individual user’s interest profileA data representation of an individual's interests based on site activity, derived from the near real-time modeling of topics contained within the URLs that the individual visited. in real-time. As the user’s interest profile changes with more content consumption, the corresponding recommendations change appropriately.

Content Recommendations has many evaluation tools to help you improve your content. For example, the topic performance chart shows how much interest there is in a topic. See Topic performance.

Image: Topic performance chart

You can use Content Recommendations with the rest of Optimizely in some following example ways:

Terminology

The following terms are associated with Content Recommendations.