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Oracle® Application Server Personalization User's Guide
10g Release 2 (10.1.2) B14052-01 |
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Oracle® Application Server Personalization User's Guide
10g Release 2 (10.1.2) B14052-01 |
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The Oracle Application Server Personalization (OracleAS Personalization) Administrative UI is a browser-based user interface that permits the OracleAS Personalization administrator to manage RE Farms and REs, schedule and deploy predictive model packages, produce reports, and otherwise manage OracleAS Personalization.
This chapter provides an overview of the Administrative UI and then illustrates how to use the UI to obtain recommendations.
Bring up the OracleAS Personalization Administrative UI by typing the following in the URL field of your browser:
http://<hostname>:<port>/OP/Admin/
where <hostname> is the name of the system on which Oracle Application Server is installed and <port> is the port number assigned to the OracleAS Personalization Administrative UI pages.
Note that the URL is case-sensitive.
The Web pages available in the Administrative UI are as follows:
Enter a user name and password for the MOR schema (a valid user name and password were established during OracleAS Personalization installation; if you don't know what they are, ask the person who installed OracleAS Personalization).
After entering a valid user name and password, click Log in, which brings up the OracleAS Personalization Administrative home page.
The Home page welcomes you and briefly describes the product. At the left are links to common tasks, such as create new farm, create package, schedule a build. They are listed in the order you would follow in executing the tasks. To the right is a brief status of recent events (builds, deployments, and reports).
Browse the pages to get familiar with their structure and content. Click the tabs to see the various pages. The tabs are Home, Farms, Packages, Schedules, Reports, and Log. The tab pages organize OracleAS Personalization administrative tasks.
The Farms page lists the current recommendation engine farms. Use the Farms page to create, delete, and in general manage recommendation engine farms.
The Farms page gives access to recommendation engines, which you add to a farm. Adding a recommendation engine to a farm means specifying the RE's database connection details. These details were established during the installation of the database.
An RE must have a connection to the MTR. If there is no pre-existing MTR connection, you create one and give it a name. This also requires information established during installation and configuration of the database.
The Packages page lists current packages. Use this page to create, delete, and manage packages.
An OracleAS Personalization package contains all the information needed for building predictive models, which includes the general settings for the package, information about its connection to the MTR, and settings specified for the package's build (which includes settings for building the model).
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Note: A package does not contain build schedule information. This information is in the MOR. |
To create a package, click Create Package. This brings up the first of three pages that guide you through creating a package:
The first page, Create Package, prompts you for a name and description of the package and asks you to specify the MTR connection by name. If there is no MTR connection, you are prompted to create one.
The second page, Specify Build Settings, prompts you for the build settings, which include
Recommendation performance: This is a trade-off between accuracy and speed — that is, between higher recommendations accuracy and faster recommendations.
New products: Specify whether you want OracleAS Personalization to use a proxy for new products. There must be proxy information in the MTR to use this option.
Choose what time-stamped session data to use: Use data from any dates, including data with no dates; a rolling window of the past x number of days, or between specified dates.
The third page, Confirm Settings, summarizes and confirms the settings you have specified. The Confirm Settings page also gives you the option of going directly to scheduling a build for the package.
Click the Schedules tab and note the three kinds of events that are scheduled: Builds, deployments, and reports. The Schedules tab opens by default to the Build Schedule page (see the next section).
Use this page to create, edit, delete, and in general manage the building schedule of a package, that is, the creation of predictive models and other information needed to make recommendations.
To create a new build schedule, click Create Build Schedule. The Create Build Schedule page gives you the following options:
Build the package as soon as possible.
Build the package at a future time that you specify, and with the frequency you specify. When a package is built, it is stored in the MOR, ready for deployment.
This page also gives you the option of going directly to scheduling deployment of the package (see the next section).
Use this page to create, edit, delete, and in general manage deployment schedules.
Deploying a package means copying it to every RE on a given RE Farm. Multiple REs with the same package can share the load.
To create a new deployment schedule, click Create Deployment Schedule. The Create Deployment Schedule page gives you the following options:
Deploy the package every time it is built.
Deploy the package as soon as possible.
Deploy the package at a future time that you specify, and with the frequency you specify.
Use this page to create and manage report schedules.
To schedule a report, click Create report schedule.
The Create Report Schedule page gives you the following options:
General settings: Specify the RE Farm, the report type, the email address of the person who should receive notification, and any notes you may wish to make.
Data selection: Use data from any dates; a rolling window of the past x number of days, or between specified dates.
Schedule: Run the report as soon as possible or schedule the run for a specified date with specified frequency.
The Create Report Schedule page also lets you do the following:
To update or look at the details of a report schedule, click its Edit icon.
To delete a scheduled report, select it and click Delete.
To stop a scheduled report that is running, select it and click Stop.
Note: You cannot edit or delete a scheduled report while it is running or canceling; it must be idle.
Use this page to view reports. There are three types of reports:
Purchasing session report: Reports, for a user-specified period of time, the total number of sessions, the number of sessions that had at least one purchase, and the percentage of purchasing sessions to total sessions.
The total number of sessions includes both visitor and customer sessions. Sessions that had purchases include only customer sessions.
Recommendation effectiveness report: Reports, for a user-specified time period, the number of recommendation requests served by the system, the number of recommended items clicked per recommendation request, and the number of recommended items purchased per recommendation request.
This report includes all clicked and purchased items in its "Clicked" and "Purchased" columns, even those items that are not part of the recommendations.
Itemized recommendation effectiveness report: Reports, for a specified time period, how many times the items have been recommended, how many times they have been clicked, and how many times they have been purchased.
Entries in the "Product ID" column have the following format:
<Product type> : <Product id>
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Note: The MTR tables MTR_SESSION and MTR_RECOMMENDATION_DETAIL must be populated before a report can be generated. |
You must deploy a package before you can execute a Java program that requests recommendations or collects data.
Therefore, before you can obtain recommendations you must:
Create a Recommendation Engine Farm.
Add at least one RE to the Farm.
Populate at least the minimum data required in the MTR tables. You can do this with the demo data that is loaded at the time of installation.
Schedule a build and deployment.
This chapter illustrates performing these steps.
Note that you cannot build a package unless there is data available; see Section 1.7.1, "Predictive Models Built by OracleAS Personalization" for more information.
The first step is to create an RE Farm. There are two ways to start:
On the Home page, click the Create new farm link to bring up the Create Recommendation Engine Farm page.
Click the Farms tab to go to the Farms page. On the Farms page, click Create Farm (lower right), which brings up the Create Recommendation Engine Farm page.
On the Create Recommendation Engine Farm page,
Enter a name for the farm.
On the Add Recommendation Engine page, enter a name for the recommendation engine.
For the database connection details, you will need information that was provided during installation.
For Host ID, Port, SID, and Database alias, you will need the database information you provided when you installed the database.
For DB schema name, User name, and Password, you will need the information provided when you installed and configured the RE schema.
After filling in all the fields, click Test Connection to determine whether the database connection is successful, that is, to verify that the information provided can be used to make a successful connection to the desired schema.
Assuming the database connection is successful, click OK (lower right), which takes you back to the Create Recommendation Engine Farm page, where you will see listed the recommendation engine you defined.
Note: If you click Cancel instead of OK, the information you have entered is lost.
Next, create a package.
To create a package, you must have a connection to the MTR. If you do not have an MTR connection,
Click Options (upper right) and go to the MTR database connections section. The information needed for an MTR connection comes from entries provided when you installed and configured the MTR schema.
To check the database connection, click Test Connection.
Assuming the connection is successful, click OK, which returns you to the Create Farm page.
Now you can create a package:
Click the Packages tab, which brings up the Packages page.
On the Packages page, click Create Package (lower right), which brings up the first of three pages of the Create Package wizard.
Give the package a name (required), a description (optional), confirm the name of the MTR connection, and click Next.
Specify the settings to be used to build and tune the new package, and click Next.
Review the settings you have specified, leave the Schedule a build box checked, and click Finish. This takes you to the Create Build Schedule page.
On the Create Build Schedule page, select
Build as soon as possible.
Leave the Schedule deployment box checked.
Click OK. This takes you to the Create Deployment Schedule page.
On the Create Deployment Schedule page,
Select Deploy every time the package is built.
Leave the default for Frequency, Once.
Click OK. This returns you to the Packages page.
You have created an RE Farm with one RE, and you have created a package and scheduled its build and deployment.
Check the Packages page after a few minutes to see whether the package has yet built and deployed. The Packages page displays the status; refresh the page by clicking Go.
When the package has built and deployed successfully, you can use it to collect data and make recommendations using the Recommendation Engine API.
Next, browse the contents of the Mining Table Repository (MTR) database used to build the predictive model. This is the pre-populated MTR that is installed when OracleAS Personalization is installed if you select that option. This pre-populated MTR provides the data needed to perform the exercises described in this manual.
Use SQL*Plus commands to examine the contents of any of the database tables. Table 2-1 shows what part of one of the MTR database tables looks like. It contains movie ratings by customers, demographic data on those customers, an ID for each movie that was rated, the rating given the movie by the customer, and the data source type.
Table 2-1 Sample MTR Database Table
| CUSTOMER_ID | ITEM_ID | ITEM_TYPE | ATTRIBUTE_ID | BIN_VALUE | DATA_SOURCE_TYPE |
|---|---|---|---|---|---|
|
2 |
264 |
MOVIE |
1 |
1 |
2 |
|
2 |
389 |
MOVIE |
2 |
1 |
3 |
|
2 |
153 |
MOVIE |
2 |
2 |
3 |
|
2 |
354 |
MOVIE |
2 |
2 |
3 |
|
2 |
264 |
MOVIE |
2 |
3 |
3 |
|
2 |
153 |
MOVIE |
3 |
1 |
4 |
|
2 |
264 |
MOVIE |
3 |
1 |
4 |
|
2 |
354 |
MOVIE |
3 |
1 |
4 |
|
2 |
389 |
MOVIE |
3 |
1 |
4 |
|
2 |
0 |
NONE |
4 |
3 |
1 |
|
2 |
0 |
NONE |
5 |
1 |
1 |
|
2 |
0 |
NONE |
6 |
2 |
1 |
This sample comes from a large database table that contains movie ratings by customers, demographic data on those customers, an ID for each movie that was rated, and the rating given the movie by the customer. Table columns are as follows:
CUSTOMER_ID: The data is all from customer with CUSTOMER_ID = 2.
ITEM_ID: The ID numbers identify particular movies. Demographic information for a customer always has an ITEM_ID of 0.
ITEM_TYPE: Refers to kind of item being rated; here the type is movie. Demographic information for a customer always has an ITEM_TYPE of NONE.
ATTRIBUTE_ID: Values 1, 2, and 3 are attributes from purchasing, rating, and navigational data; values 4, 5, and 6 are from demographic data (the last three rows), where 4 = age, 5 = gender, and 6 = marital status.
BIN_VALUE: Ratings were binned in 3 bins; the values here correspond to those bins.
DATA_SOURCE_TYPE: (what kind of data) 1 = demographic, 2 = purchases, 3 = ratings, 4 = navigational.
For more information about the OracleAS Personalization schemas, see the Oracle Application Server Personalization Administrator's Guide.
