Design a feedback-text personalization data system

Q: Design a feedback-text personalization data system

This question evaluates skills in designing end-to-end ML systems for personalization from free-text feedback, covering competencies in NLP feature representation (embeddings), data modeling, retrieval, pipeline orchestration, model selection, and scalability within the ML System Design domain.

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Question

System/ML design scenario: personalize using user feedback text

You are building an embeddings + personalization pipeline for a consumer product (e.g., genealogy/ancestry). Users provide free-text feedback (comments, reviews, support tickets) about products/features.

Design a system that:

Ingests and stores user feedback text so it can be queried and used for modeling.
Defines features/representations (e.g., embeddings) to find similar users and/or similar products from feedback.
Supports automatic updates of “input categories” (e.g., tags/topics/issue categories) as new kinds of feedback appear.

Assume moderate scale (millions of users, tens of millions of feedback records). Discuss data model, pipelines, model choices, retrieval, updates, and evaluation.

Design a feedback-text personalization data system

Quick Overview

System/ML design scenario: personalize using user feedback text

Solution

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