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Portal Personalization

Portal Personalization provides automatic customization of website content for individual users and user groups.

Personalization can recognize a specific user based on a profile or can determine characteristics of a user based on previous purchases, products or pages viewed, and so forth. Personalization then 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 website premium product. If an individual belongs to a particular geographic 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

The engines rules engine, together with the recommendation engine and the resource engine, are sometimes collectively referred to as the Personalization run time 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 website premium product. The page is assembled with the proper personalized information. The user sees her personalized page.


Types of Personalization

There are three types of Personalization:


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 preexisting 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.

Sequence: