I went straight to user satisfaction metrics and started talking about watch time, which I think was the wrong instinct.
Start by clarifying the goal of the recommendation algorithm (e.g., maximize long-term user satisfaction, not just watch time) and the target user segment. Then identify key problems with the current system (e.g., filter bubbles, clickbait, lack of diversity) and propose data-driven improvements with measurable success metrics. Structure your answer around a clear framework: define objectives, diagnose issues, ideate solutions, prioritize, and measure impact.
Pro tip: Acknowledge the trade-offs between different objectives (e.g., engagement vs. user well-being) and propose a balanced approach with guardrail metrics. This shows you understand Google's responsibility at scale and can think beyond short-term metrics.
Ask clarifying questions to understand the primary objective (e.g., increase long-term user satisfaction, reduce harmful content, improve diversity) and the target user segment. Confirm whether the focus is on a specific surface (e.g., home feed, watch next) or the entire recommendation system.
Identify key problems with the current algorithm, such as filter bubbles, over-reliance on click-through rate, promotion of clickbait, lack of content diversity, or failure to surface fresh creators. Use data or user research to support these issues.
Brainstorm specific changes, such as incorporating long-term satisfaction signals (e.g., surveys, session duration), diversifying recommendations, adding user controls (e.g., sliders for content preferences), or using reinforcement learning to optimize for multiple objectives.
Assess each idea based on impact, effort, and alignment with company goals. Consider technical feasibility, potential risks (e.g., reduced engagement), and how to A/B test the changes.
Propose a set of metrics to measure success, including primary metrics (e.g., user satisfaction scores) and guardrail metrics (e.g., watch time, retention). Outline a plan for iterative testing and learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.