Glossary

Admin UI (Administrative UI)

A graphical user interface that enables you to manage OracleAS Personalization, which includes (1) scheduling the build and deployment of packages and the generation of reports, and (2) managing the creation and use of recommendation engines (RE) and RE Farms.

Algorithm

See Recommendation Algorithms.

Category

A group of similar items. A category is an element in a taxonomy; an abstraction for a group of items or categories. In OracleAS Personalization, any item or category can belong to one or more categories but not to itself, directly or indirectly. See also Taxonomy.

Category Membership

Category membership specifies how items and categories are related to other categories. For example, an item can have a SUBTREELEAF relation to a category if it is a descendant of that category. Similarly, a category can have a SUBTREENODE or LEVEL relationship with another category. See also Taxonomy.

Cross-Sell Model

Cross-selling is done when a customer shows evident interest in a certain item and recommendations for other items are made to him or her. The cross-sell model is used only in the cross-sell methods. It allows only either navigational or purchasing types of data source for input, and requires that the interest dimension be directly related to the type of input data source.

Data Source Types

OracleAS Personalization uses data mining data of the following types: navigational, ratings, purchases, and demographics.

Demographics

The particular demographic attributes of interest to OracleAS Personalization are listed below. They are stored in the MTR in the CUSTOMER table/view, which consists of the following fields.

CUSTOMER_ID

NAME

GENDER

AGE

MARITAL_STATUS

PERSONAL_INCOME

HEAD_OF_HOUSEHOLD_FLAG

HOUSEHOLD_INCOME

HOUSE_HOLD_SIZE

RENT_OWN_INDICATOR

ATTRIBUTE1 - ATTRIBUTE50: These are generic attributes that can be mapped to any column in the customers' database or can be null. They provide extra flexibility. The first 25 are for string (VARCHAR2) data; the last 25 (26-50) are for numeric data.

Deployment

The process of transferring the tables that define a predictive model to one or more recommendation engines after the model has been built. A deployment also establishes the necessary connections between the recommendation engine and the MTR. The recommendation engine can run in a separate database instance from MTR and MOR if the site administrator chooses.

Farm

See Recommendation Engine Farm (RE Farm).

Hot Picks

On some e-commerce sites, vendors promote certain products called "hot picks"; the hot picks might, for example, be this week's specials. The hot pick items are grouped into hot pick groups.

I-I (Item-to-Item)

"I-I" is encountered in some detailed error messages. It stands for Item-to-Item.

Interest Dimension

Specifies the interest dimension that items should be ranked against. The interest dimensions supported in OracleAS Personalization are rating, purchasing, and navigation.

Mining Object Repository (MOR)

The MOR is the Oracle database schema that maintains mining metadata defined by the OracleAS Personalization data mining schema and provides for logging in to the data mining system, logging off, and validating users for the MOR and data mining functionality. Provides core data mining algorithm functionality.

Mining Table Repository (MTR)

The MTR is a schema containing the data used for data mining. It contains all the data necessary to define and build a package. For OracleAS Personalization, the MTR has a fixed schema designed to support the building of models that support producing customer/visitor recommendations. The MTR also stores customer data collected through the REAPI.

OP

Oracle Application Server Personalization, also OracleAS Personalization.

Personalization Index

The relative degree of individualization desired in OracleAS Personalization's recommendations. A high setting produces the most individualized recommendations, those most highly related to the given user profile. A low setting generates recommendations that are the most popular or common for a given user profile. A low setting would yield "best seller" kind of recommendations, whereas a high setting will produce recommendations that may not be appropriate for many people, but the recommendations may be of higher perceived value.

P-I (Person-to-Item)

"P-I" is encountered in some detailed error messages. It stands for Person-to-Item.

Package Settings

The parameters that instruct the MOR algorithms how to build the predictive models. See Predictive Model and Predictive Model Package.

Predictive Association Rules Alogrithm

This is a recommendation algorithm used by OracleAS Personalization (see Recommendation Algorithms). This algorithm's characteristics are:

• Models are based on association rules

• Builds models and scores datasets inside the database

• The mined rules, which form the basis of the predictive models, are stored in database tables

• The rule consequents are combined and ordered using a ranking function

• Consequents with higher ranks are returned as recommendations

Predictive Model

A model is a compact representation of the knowledge or patterns found in a particular dataset. In OracleAS Personalization, it consists of a set of tables containing all the data necessary to make recommendations. See also Recommendation Algorithms.

Predictive Model Package

An object created using the Administrative UI that controls predictive model building. A predictive model package consists of the following:

• predictive models for Predictive Association Rules and Transactional Naive Bayes algorithms

• MTR synchronization settings

• customer data synchronization interval

• time-out sessions period

Predictive model packages are deployed to REs, perhaps via deployment to RE farms.

Profile

All the data collected about a customer from that customer's sessions. These data are: navigational, ratings, purchases, and demographic data. Profiles are stored in the MTR.

Rating scale

The rating scale for OracleAS Personalization should be in ascending order of "goodness". That is, create a scale in which a high rated item indicates a prediction that the user prefers that item over items with lower ratings.

Recommendation Algorithms

OracleAS Personalization bases its recommendations on one of two algorithms: Predictive Association Rules and Transactional Naive Bayes. For fuller descriptions of these algorithms, see Predictive Association Rules and Transactional Naive Bayes, in Appendix (UNKNOWN STEP NUMBER) .

Recommendation Engine (RE)

The front end of OracleAS Personalization. It is a set of PL/SQL routines that run in a database instance. Through the Recommendation Engine Application Programming Interface, RE provides the following services on a Web server associated with the calling Web application:

• Collects and pre-processes users' profile data

• Returns personalized recommendations

Recommendation Engine API (REAPI)

A collection of Java classes that enable a Web application written in Java to collect and preprocess data used to build OracleAS Personalization models and to produce recommendations from OracleAS Personalization.

Recommendation Engine Farm (RE Farm)

A group of REs where the REs have identical deployment of predictive model packages. This grouping improve scalability and reliability of applications using OracleAS Personalization.

Recommendations

The following are characteristics of recommendations made by OracleAS Personalization:

• Uses demographics, taxonomies, data from past sessions, current session data

• Performs efficient and scalable scoring

• Builds predictive models offline

• Handles both registered customers and anonymous visitors

• Are generated from predictive models, which are in turn generated from using the Transactional Naive Bayes or Predictive Association Rules algorithm

Schedule Item

An object created using the Admin UI that controls when predictive models specified by a package are to be built or deployed, or when a report is to be generated.

Score

With reference to applying a predictive model to new data, scoring means assigning a score that reflects the confidence in a prediction. Score is also a prediction + confidence of that prediction. Confidence is expressed as a number.

Session

Sessions are used to organize user activities. A session corresponds to a set of activities that a user does in "one sitting". Each session is uniquely associated with a user and has a start_time and end_time. All the activities performed by that particular user within the (start_time, end_time) interval are considered to be part of that session.

SID

See System Identifier (SID).

System Identifier (SID)

An identifier for an Oracle database instance. In OracleAS Personalization, it refers to the unique identifier assigned to each system associated with an MOR. Each system attached to an MOR must have a unique identifier specified in its configuration file.

Taxonomy

In the OracleAS Personalization context, this term refers to the structural organization of items in a company's catalog or site. Typically, the catalog or site or both has a hierarchical structure, with the most general category at the base (for example, "clothing"), and branching to increasingly specific categories (for example, from "clothing" to "shoes" to "sneakers" to "tennis shoes").

Items can belong to more than one category and to different levels of the structure. For example, "tennis shoes" can be a category in "clothing" and also a category in "sports equipment."

The structure of the OracleAS Personalization taxonomy is a DAG (direct acyclic graph), which can contain multiple top-level nodes. The different portions of the taxonomy can be disconnected. Any node can connect to any other node but there cannot be a path that connects a node's child back to the node itself.

OracleAS Personalization supports multiple taxonomies. A taxonomy is implemented using a group of tables in the MTR (they are specified at installation time).

Transactional Naive Bayes

This is a recommendation algorithm used by OracleAS Personalization (see Recommendation Algorithms). This algorithm's characteristics are:

• Models are based on conditional probabilities

• Builds models and scores datasets inside the database

• Probabilities are combined according to Bayes' Theorem and a score is computed

• Products with higher scores are returned as recommendations

User (of OracleAS Personalization)

The user of OracleAS Personalization is a DBA or system administrator or Java programmer, or perhaps all three. Do not confuse this with the user of a Web site that uses OracleAS Personalization.