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Collaborative filtering
Collaborative filtering is a method of calculating the similarities among a group of customers. The collaborative filtering application considers site usage behaviors of the group of customers that have the most in common with the current user as a base for generating predictions and recommendations for that user. As a simple example, if a particular customer looks at a book, the collaborative filtering application determines the group of customers that have either looked at, or purchased the same book. The application then generates a list of possible recommendations from the other items that the members of the established group have also looked at or purchased. The relevance of any particular recommendation is determined by the number of group members that have looked at the item being recommended. That is, if 75% of the group have purchased the item being considered for recommendation, then it would be highly relevant, compared to an item that only one member of the group has looked at. Finally, the most relevant recommendations are displayed to the current customer.
Practical implementations of collaborative filtering are more complex than the preceding description. Many use multiple data sources which may include the following:
- Clickstream events that capture the details of a customer's session
- Data from existing company databases
Collaborative filtering is implemented in WebSphere Commerce by LikeMinds software.
Related concepts
WebSphere Commerce Recommendation Engine powered by LikeMinds