Sharath Kumar RK, Balaji Kadambi, and Manjula Hosurmath also contributed to this blog post.
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 is what we try to solve with a new developer pattern, wherein we use each customer’s browsing pattern and order history to serve up a highly personalized list of products on the WebSphere Commerce Aurora store. We differentiate between strong biases and soft preferences for each customer using our model. This data is then used for applying filtering and search boosting for that specific customer. This ensures that there is true personalization and no generalization.
You may use the same logic and apply your search to a content site, too. We are not replacing the Solr engine here, so there is no drastic re-platforming. Although we have implemented this specifically for personalizing search results, the same logic can be applied to site navigation, curation of recommendations, and creating dynamic menus/categories for each customer.
Check out the “Deliver optimized, personalized search results” pattern, which includes demos, code, and more.