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Deliver optimized, personalized search results


Search is an integral component of most websites — content or commerce. The search personalization capabilities available in commercial off the shelf and generally implemented are still rudimentary rule-based behavior and cater to a large set of users and lack personalization. In this developer pattern, you will be able to gauge the user’s context and intent to deliver an optimized, personalized search result and reduce the number of clicks for a user to get to the content or product.


What if a commerce system could understand our preferences and choices and serve up different search results, based on the same? Is it fair to serve up the same search result just because the search terms are same? How can we bring in customer’s context and intent in personalizing the search result? This pattern demonstrates a methodology to personalize search results by identifying clear-cut affinities/preferences across various categories customers have previously ordered from.

The intended audience for this pattern includes architects and senior developers who want to deliver personalization to their products and content search functionality. When you have completed this pattern, you will understand how to develop search personalization and boost search results in accordance with each customer’s preferences, using the IBM WebSphere® Commerce and IBM Predictive Customer Intelligence.



  1. User initiates search in WebSphere Commerce storefront.
  2. User profile data is exported from WebSphere Commerce to a file repository.
  3. Order data is exported from WebSphere Commerce to a file repository.
  4. User profile data is imported into IBM Predictive Customer Intelligence from the file repository for analysis.
  5. Order data is imported into Predictive Customer Intelligence from the file repository for analysis.
  6. Predictive Customer Intelligence models establish affinity for each user across various categories based on the order data from WebSphere Commerce. User data enriched with scores and affinity attributes is churned out from Predictive Customer Intelligence.
  7. Predictive Customer Intelligence user-affinity data is fed into WebSphere Commerce and used to enrich the existing search, and affinity data is used to filter the search results.


Ready to put this code pattern to use? Complete details on how to get started running and using this application are in the README.