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by Priya Vasudevan, Sharath Kumar RK, Balaji Kadambi, Manjula Hosurmath | Updated May 22, 2018 - Published May 21, 2018
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.
Ready to put this code pattern to use? Complete details on how to get started running and using this application are in the README.
March 21, 2019
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