Personalization

Personalization

Personalization can recognize a specific site visitor based on a profile. It can also determine characteristics of a user based on previous purchases, products or pages viewed, or other attributes. If a visitor belongs to a particular geographic region, content specific to that region can be targeted to the visitor. The page is assembled with the personalized information, and the visitor sees a personalized page.

Personalization tools

Personalization browser:
Register resource collections Author rules, campaigns, and content spots Map rules into content spots for a particular campaign

Rules engine:
Use rules created in the Personalization browser. The Personalized List portlet or Web Content Manager can start Personalization rules. We can also use the Personalization API to start rules. Rules associated with pages or portlets through Portal Administration are automatically triggered.

LikeMinds Recommendation engine:
Evaluate recommendation rules created in the Personalization browser.

Resource engine:
Resolve the queries produced by rules into content pieces to be returned. Content for Personalization is created and approved using whatever content management tool we choose, or from an LDAP, or any other database. Content is accessed using a set of Resource Collection classes.

A logging framework:
Record information about website usage to the feedback database and the recommendation engine. It is entirely up to the site developers to decide what information is logged.

The engines are sometimes collectively called the Personalization run time server.
The engine identifies the particular user. Personalization retrieves user profile. For example, a user profile might include salary range information. If a user has a high salary range, we can configure Personalization to show information about a premium product on the website.

Types of Personalization

There are three types of Personalization:

Simple filtering A site renders content based on predefined groups of site visitors. For example, if a site visitor is in the Human Resources department, the site provides access to URLs containing Human Resources policy manuals.
Rules engines In a rules-based system, the site owner defines a set of business rules. The rules determine what category of content is shown when a certain profile type visits the site. An example would be to show all four-wheel drive SUVs to visitors in the northeast in the 21-to-35 age group. Use this approach to drive the site behavior based on the business objectives. The site owner is usually our owner of a marketing campaign or some other business manager.
Collaborative filtering A site visitor rates a selection of products, explicitly or implicitly. Those ratings are compared with the ratings offered by other visitors. Software algorithms detect similarities. For example, a visitor receives book recommendations based on the similar purchases of others.

See

Content that you personalize
Create a business rule
Link the rule to a content spot on the site
Publish the rules to production
Personalization

Parent Applications

Simple filtering	A site renders content based on predefined groups of site visitors. For example, if a site visitor is in the Human Resources department, the site provides access to URLs containing Human Resources policy manuals.
Rules engines	In a rules-based system, the site owner defines a set of business rules. The rules determine what category of content is shown when a certain profile type visits the site. An example would be to show all four-wheel drive SUVs to visitors in the northeast in the 21-to-35 age group. Use this approach to drive the site behavior based on the business objectives. The site owner is usually our owner of a marketing campaign or some other business manager.
Collaborative filtering	A site visitor rates a selection of products, explicitly or implicitly. Those ratings are compared with the ratings offered by other visitors. Software algorithms detect similarities. For example, a visitor receives book recommendations based on the similar purchases of others.