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In the video, I discussed how Flipkart personalized its typeahead search suggestions, focusing on highlevel architecture and key design decisions. To enhance user experience, personalized suggestions based on user history were prioritized. Factors such as suggestion quality, performance, and grammar were considered for ranking. User intent was analyzed using product taxonomy, past searches, and browsing history. Personalization was achieved by training a model using user interactions. The system utilized XGBoost with LTR ranking in a search engine like Solar for efficient suggestion delivery.

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