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LikeMinds Recommendations

Personalization contains a dynamic recommendation system based on LikeMinds, which is software used with the e-commerce applications. LikeMinds analyzes user interactions and generates real time predictions and recommendations to the Web site users.

Predictions are generated by three LikeMinds engines using recommendation rules based on transactions logged through rating and action beans.

If the e-commerce Web site is set up so that users can rate content or products, we use Rating beans to capture rating data. If we use shopping cart technology, action logging beans are used to capture content affinity behavior. Both rating and action data is stored in the database.

For example, the following types of transactions may be recorded:

  • Products a user has purchased
  • Items added or removed from a shopping basket
  • A history of the user's navigation throughout the application
  • Products that go best with a product that the user has already selected
  • Any action or series of actions that are meaningful for a site


Mentors

Typically, after a user has rated a minimum number of items or completed a minimum number of transaction activities, that user is assigned a set of mentors. A mentor is a specially designated user who has visited the e-commerce application a number of times, and whose profile is similar to the user's. LikeMinds uses a technique called collaborative filtering to build a mentor's profile for each user to predict how much a user will like particular items and which items that user will enjoy, buy, or add to their shopping cart.

Predicting a matching product to go with a user's selected product, independent of actual user preferences, is accomplished by the discovery of probable pairs of product matches to be recommended. This concept is called item affinity and uses a family of algorithms different from collaborative filtering. While collaborative filtering uses its algorithms to discern the highly variable affinities between individual Web-surfers, the item affinity approach looks at relationships that can exist between items.


See also

  1. LikeMinds Recommendation Engine architecture
  2. How LikeMinds generates recommendations
  3. The LikeMinds Recommendation Engines
  4. The LikeMinds utilities
  5. Configure LikeMinds
  6. MovieSite Sample
  7. Use the LikeMinds utilities
  8. Filtering LikeMinds recommendations


Parent topic: Personalization

Previous topic: The Portal User resource collection

Next topic: Feedback and analytics


Related information


LikeMinds Recommendation Engine architecture