I started with two-tower retrieval and they seemed fine with that, but then they drilled into why I'd separate retrieval from ranking at all.
Start by clarifying requirements and scale, then propose a two-stage architecture with candidate retrieval and ranking. Detail the model choices, features, and trade-offs, emphasizing how you'd evaluate and iterate.
Pro tip: Show awareness of the cold-start problem and how you'd handle new restaurants and users, as this is a common real-world challenge. Also, mention how you'd incorporate Snapchat's unique data like location and social graph.
Ask about scale (users, restaurants), latency requirements, and personalization needs. Confirm if it's for mobile, web, or both, and if real-time updates are needed.
Propose a two-stage system: retrieval to get candidate restaurants (e.g., thousands) and ranking to order them (e.g., top 10). Mention offline training and online serving.
Describe methods like collaborative filtering, content-based, or two-tower models to retrieve candidates based on user and context. Discuss indexing and approximate nearest neighbor search.
Explain a ranking model (e.g., GBDT or deep neural network) that takes features from user, restaurant, and context to predict relevance (e.g., click-through rate or rating).
List key features: user (history, demographics), restaurant (cuisine, price, location), context (time, location, device). Discuss offline metrics (NDCG, recall) and online A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew this was coming and still didn't have a crisp answer ready.
Start by clarifying the system context—likely a recommendation or ranking system at Snapchat involving users and restaurants (e.g., Snap Map or food delivery). Then, address cold-start separately for users and restaurants, proposing a combination of content-based, contextual, and popularity-based fallbacks, and finally discuss how to transition to personalized models as data accumulates.
Pro tip: Emphasize that cold-start is not a one-time problem but a continuous challenge, and propose a unified framework that handles varying degrees of data sparsity, including meta-learning or transfer learning from similar domains (e.g., other business types on Snapchat).
Ask clarifying questions about the system: what signals are available (e.g., user demographics, location, restaurant attributes), and what defines a 'new' user or restaurant (zero interactions vs. limited).
Propose using contextual and demographic features (age, location, time of day) and popularity-based recommendations initially, then quickly incorporate real-time interactions (e.g., clicks, views) via online learning.
Leverage restaurant metadata (cuisine, price, location, images) and content-based filtering, plus explore-and-exploit strategies (e.g., multi-armed bandits) to gather initial feedback efficiently.
Propose a hybrid model that combines content-based, contextual, and collaborative signals, with a fallback hierarchy (e.g., global popularity → segment popularity → personalized) and a smooth transition as data grows.
Define metrics (e.g., CTR, conversion, diversity) and A/B test cold-start strategies, monitoring for biases and ensuring fairness across new users and restaurants.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through AUC and NDCG for offline, CTR and session engagement for online.
Start by defining the recommendation system's goal (e.g., increasing engagement or satisfaction) and then outline offline metrics that measure model quality, online metrics that measure business impact, and how offline improvements should translate to online gains. Emphasize the importance of aligning offline and online metrics through experimentation and iteration.
Pro tip: Highlight the limitations of offline metrics and the need for online validation, and mention that at Snapchat, metrics like CTR and watch time are often used but must be balanced with user satisfaction and long-term retention.
Identify the primary goal of the recommendation system, such as increasing user engagement, satisfaction, or revenue, to select appropriate metrics.
List offline evaluation metrics like precision@k, recall@k, NDCG, MAP, and AUC that measure the model's ranking quality and predictive accuracy.
List online metrics such as click-through rate (CTR), conversion rate, watch time, daily active users (DAU), and retention that measure real-world impact.
Explain how offline metrics serve as proxies for online performance, and how improvements in offline metrics should lead to gains in online metrics, validated through A/B testing.
Acknowledge potential trade-offs (e.g., optimizing CTR might hurt long-term retention) and the need for continuous monitoring and iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about implicit signals like clicks and dwell time versus explicit ratings.
Start by clarifying the ML problem and Snapchat's product context (e.g., ranking, recommendations, AR effects), then propose specific data sources and labeling strategies. Structure your answer around key tradeoffs like cost, quality, bias, and latency, and tie them to business impact.
Pro tip: Emphasize that data collection and labeling are iterative and should be guided by offline metrics and online A/B tests; mention how you'd handle Snapchat's unique multimodal data (images, video, text) and privacy constraints.
Ask clarifying questions to understand the specific ML task, success metrics, and constraints (e.g., latency, privacy). Identify what types of data (user interactions, content, context) are required.
List potential data sources (e.g., user logs, public datasets, synthetic data) and how to collect them (e.g., implicit feedback, surveys, crowdsourcing). Consider Snapchat-specific data like Snaps, Stories, and Lenses.
Describe labeling approaches (manual, automatic, weak supervision) and tools (e.g., Labelbox, internal tools). Discuss how to ensure label quality and handle ambiguous cases.
Discuss tradeoffs such as cost vs. quality, scale vs. accuracy, bias vs. representativeness, and privacy vs. personalization. Relate these to Snapchat's scale and user expectations.
Explain how you would evaluate the data (offline metrics, online A/B tests) and iterate on collection/labeling based on model performance and business impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.