Personalization Overview

 

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Personalization lets Web sites customize their content automatically for each user. Personalization can recognize a specific user based on things like...

  • profiles
  • previous purchases
  • pages viewed

Personalization selects content that is appropriate for that profile. If a person has a high salary range, Personalization can be configured to retrieve information about a commercial Web site's premium products. If an individual belongs to the "Eastern region," content specific to that region may be targeted to the individual. The page is assembled with the proper personalized information, and the user sees her personalized page.

Personalization is composed of

  • Personalization browser

    Since objects are authored through the Personalization server, the Personalization browser can display rules in production as well as view rules in a staging environment.

  • Rules engine

    The rules engine executes rules created in the Personalization browser. A programming interface exists for Personalization to invoke rules, Personalization rules may be invoked through the Personalized List portlet, or rules may be invoked through Web Content Management Personalization components. Rules associated with pages or portlets through Portal Administration are also automatically triggered.

  • LikeMinds Recommendation engine

    The recommendation engine evaluates recommendation rules created in the Personalization browser.

  • Resource engine

    The resource engine resolves the queries produced by rules into content pieces to be returned. Content for Personalization is created and approved using whatever content management tool you choose, or may come from an SQL, LDAP or any other database. Content is accessed via a set of Resource Collection classes.

  • A logging framework

    The logging framework is used to record information about web site usage to the feedback database and the recommendation engine. It is entirely up to the site developers to decide what information is logged.

The rules engine, together with the recommendation engine and the resource engine, are sometimes collectively referred to as the Personalization runtime server.

The engine identifies the particular user. Personalization retrieves that person's profile. For example, a person may have a salary range included in her profile. Personalization then selects content that is appropriate for that profile. If a person has a high salary range, region code, or other information, Personalization can be configured to retrieve information about a commercial Web site's premium products. The page is assembled with the proper personalized information. The user sees her personalized page.

 

Types of Personalization

There are three types of Personalization :

Simple filtering

A site displays 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 which determine what category of content is shown when a certain profile type visits the site. An example would be: Display all four wheel drive SUVs to visitors in the northeast in the 21 to 35 age group.

This approach has the advantage of driving the site's behavior with the business objectives of the site owner. The site owner is usually the 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.

 

Rules versus collaborative filtering

When complex filtering is required, a rule-based system may work better than collaborative filtering, and vice versa. The following table details examples where one type of personalization is better than the other.

Scenario Which filtering type to use Reason
If the number of items offered and users who purchase them are rather low. Rules Very little room to compute user similarity necessary for collaborative filtering.
If price points are high or purchasing frequency is low. Rules Finite, limited arenas - collaborative filtering fails because of the inherent lack of diversity.
If there is a pre-existing dependency between items.

Example: Disability policy required for homeowner

Rules Recommending a disability policy just because collaborative filtering says many others "like this user" also bought a policy is incorrect--one must have the homeowner policy first.
If number of items offered and users who purchase them are rather high. Collaborative Cannot write rules covering all items.
If price points are low, all quite dissimilar, or the products offered have a wide range of user appeal. Collaborative The wide variance fits the collaborative filtering approach. Collaborative filtering also lowers the risk of making "bad" recommendations.
When not much information is gathered about the user, but the user can be identified, possibly by a login or cookie. Collaborative In this case, user attributes on which to base rules may be lacking. Collaborative filtering can compare the user's experiences on the site to other users.