Exploring Movie Site

Exploring Movie Site

Movie Site demonstrates the LikeMinds Recommendation Engine, guiding you through a Personalization scenario that is based on factual data from an actual popular site on the Internet. The Web site uses a personalization solution to analyze visitor behavior and to recommend individualized content information and services while the visitor is actively engaged on the site.

The provided Resource Collections are for DB2 UDB only. If you wish to use the sample on other DB types, we can re-generate them using RAD. For more information on Resource Collections, see Resources, resource instances, and resource collections.

It is assumed that you are the Movie Site user, likeminds. This user has previously rated some movies and registered preferences as configured in the installation.

To set up the MovieSite sample, run the following command from the portal_server_root/config directory:

UNIX:

./WPSconfig.sh cfg-likeminds-samples -DLikemindsDbPassword=password

Windows:
WPSconfig.bat cfg-likeminds-samples -DLikemindsDbPassword=password

i5/OS:
WPSconfig.sh -profileName profile_root cfg-likeminds-samples -DLikemindsDbPassword=password
...where profile_root is the name of the WebSphere Application Server profile where WebSphere Portal is installed; for example, wp_profile.

Check the output for any error messages before proceeding with the next task. If any of the configuration tasks fail, verify the values in the wpconfig.properties file.
To start the application:

Open the WAS Administrative Console.

Go to http://machine:port/admin/ where port is the port number for WebSphere Portal.

Click Application > Enterprise Applications.

Select the MovieSite Application check box, and then click Start.

Enable logging:

In a text editor, open FeedbackService.properties.

Set loggingEnabled to true.

Restart the Personalization server.

Demo Flow Action to be Taken:
1. Navigate to the Movie Site Home Page.
For this demo you will concentrate on the recommendation engine which uses mathematical algorithms to provide collaborative filtering.
Open a separate browser window to the Movie Site home page. The URL is http://hostname:port/MovieSite where port is the port number for WebSphere Portal or Personalization

2. Log in as the likeminds user.
Log on as the user, likeminds. This login name is the default user who has registered to the site and has rated some movies.

Type likeminds in the login field.

Type likeminds in the password field

Click Enter or the Enter key.

3. Sample Previous Ratings.
Look at the movie ratings by selecting sample previous ratings. This section of the site contains a few sample movie ratings the user likeminds previously placed.

Click Sample Previous Ratings.

To the side of Rating Level, click the dropdown menu an select Get more movies.

Click the To Lobby icon.

We can show all of the movies or just certain rated movies by selecting one of the ratings. We can look at the ratings of other movies by clicking, Get More Movies. This demo allows us to rate movies. For more movies, type the name of a movie into the search panel to see if specific movies are stored in the database.
4. Use the SuperRater to rate a movie.
In Super-Rater mode, clicking the dropdown menu associated with each movie of the movie reveals a rating choice.
We can rate any movies on the list and click Submit to log the rating choice.
If you click on the movie title at the bottom of the page it will link us to information about the movie using the imdb.com site.
Notice the prediction of the recommendation engine of how much you, as the likeminds login user, would like this movie.

Click To Lobby.

Click Rate more movies.

Click Turn on Superrater.

Click on the dropdown menu associated with the rating to see the options.

Click movie title.

This will open a new window.

5. End Demonstration Click To Lobby.
The LikeMinds collaborative filtering technology considers each user to be the center of their own preference cluster, instead of trying to find the best preordained "preference category" for every user (which is typical of profile-based approaches). LikeMinds recommendations can adapt to these changes much more than aggregated, profile based techniques.

Demo Flow	Action to be Taken:
1. Navigate to the Movie Site Home Page. For this demo you will concentrate on the recommendation engine which uses mathematical algorithms to provide collaborative filtering.	Open a separate browser window to the Movie Site home page. The URL is http://hostname:port/MovieSite where port is the port number for WebSphere Portal or Personalization
2. Log in as the likeminds user. Log on as the user, likeminds. This login name is the default user who has registered to the site and has rated some movies.	Type likeminds in the login field. Type likeminds in the password field Click Enter or the Enter key.
3. Sample Previous Ratings. Look at the movie ratings by selecting sample previous ratings. This section of the site contains a few sample movie ratings the user likeminds previously placed.	Click Sample Previous Ratings. To the side of Rating Level, click the dropdown menu an select Get more movies. Click the To Lobby icon. We can show all of the movies or just certain rated movies by selecting one of the ratings. We can look at the ratings of other movies by clicking, Get More Movies. This demo allows us to rate movies. For more movies, type the name of a movie into the search panel to see if specific movies are stored in the database.
4. Use the SuperRater to rate a movie. In Super-Rater mode, clicking the dropdown menu associated with each movie of the movie reveals a rating choice. We can rate any movies on the list and click Submit to log the rating choice. If you click on the movie title at the bottom of the page it will link us to information about the movie using the imdb.com site. Notice the prediction of the recommendation engine of how much you, as the likeminds login user, would like this movie.	Click To Lobby. Click Rate more movies. Click Turn on Superrater. Click on the dropdown menu associated with the rating to see the options. Click movie title. This will open a new window.
5. End Demonstration	Click To Lobby.
The LikeMinds collaborative filtering technology considers each user to be the center of their own preference cluster, instead of trying to find the best preordained "preference category" for every user (which is typical of profile-based approaches). LikeMinds recommendations can adapt to these changes much more than aggregated, profile based techniques.