Big open-ended question and I spent probably too long on candidate generation before they nudged me toward ranking.
Start by clarifying the product goal and constraints (e.g., surface relevant Lenses to users to maximize engagement while ensuring diversity and freshness). Then walk through the ML system design in a structured way: problem framing, candidate generation, ranking model, features, labels/objective, and training/serving pipelines. Emphasize trade-offs and how you would measure success.
Pro tip: Anchor your design in Snapchat's unique context: ephemeral content, AR try-on, and social graph. Mention how you'd handle cold-start for new Lenses and users, and how you'd balance exploration with exploitation to avoid feedback loops.
Define the recommendation task: given a user and context, rank Lenses to maximize engagement (e.g., Lens usage, shares, time spent). Choose offline and online metrics (e.g., CTR, conversion rate, diversity) and set constraints (latency, freshness).
Design multiple candidate sources: collaborative filtering (user-user, item-item), content-based (Lens metadata, visual features), trending/popular, social graph (friends' usage), and contextual (time, location). Use a lightweight model to retrieve top-N candidates efficiently.
Propose a two-stage ranking: a light ranker (e.g., logistic regression or small NN) to prune candidates, then a heavy ranker (e.g., deep neural network with wide & deep or multi-task learning) to predict multiple objectives (e.g., usage, share, like). Consider sequence models for user history.
List key features: user features (demographics, past interactions, embeddings), Lens features (creator, category, visual embeddings, popularity), context features (time, location, device), and cross features (user-Lens affinity). Define labels: binary engagement (used/not used), multi-class actions, or regression on watch time. Address negative sampling and bias.
Describe offline training: data collection, feature engineering, model training (e.g., distributed training), and evaluation (A/B testing). For serving: real-time feature fetching, model inference (low latency), and post-processing (diversity, freshness, business rules). Discuss monitoring and retraining cadence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two-sided cold start in one question, which is a lot.
Start by defining the two cold-start problems separately—new Lenses and new users—then propose a unified framework that leverages content-based features and contextual signals to generate initial recommendations, followed by a bandit-based exploration strategy to gather feedback and transition to personalized models. Emphasize the importance of measuring and mitigating popularity bias and ensuring a smooth handoff from exploration to exploitation.
Pro tip: Show that you understand Snapchat's unique constraints: Lenses are consumed in short, ephemeral sessions, so cold-start solutions must deliver immediate value without relying on long-term user history. Mention that you'd leverage Snap's rich content features (e.g., Lens visual embeddings, creator metadata) and contextual signals (time, location, device) to bootstrap recommendations.
Acknowledge that cold start for Lenses and users are distinct but related: new Lenses lack engagement data, new users lack behavioral history. Highlight Snapchat's real-time, ephemeral context and the need for low-latency serving.
For new Lenses, use content-based features (visual embeddings, tags, creator info) to match with users' past preferences or contextual signals. For new users, use contextual bandits with side information (time, location, device, demographics) to make initial guesses.
Employ multi-armed bandits (e.g., Thompson Sampling) to balance exploration of new Lenses and exploitation of known preferences, while ensuring new users see a diverse set of Lenses to quickly learn their tastes.
As engagement data accumulates, gradually shift from content-based/contextual methods to collaborative filtering or deep learning models, using techniques like meta-learning or transfer learning to speed up adaptation.
Set up offline and online metrics (e.g., CTR, watch time, diversity) to monitor cold-start performance. Actively address popularity bias and feedback loops by injecting randomness and ensuring fair exposure for new Lenses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as modeling user engagement with Lenses, where each action (view, try, share, save) represents a different level of intent and value. Then, compare a single weighted model versus separate models per action, discussing trade-offs in terms of simplicity, interpretability, flexibility, and data requirements. Conclude with a recommendation based on business goals and practical constraints.
Pro tip: Emphasize that the choice depends on the specific product goals and data availability; a hybrid approach (e.g., separate models for each action combined via a learned or heuristic weighting) often balances interpretability and performance.
Clarify what each action signifies (e.g., view = low intent, try = medium, share/save = high) and how they contribute to Snapchat's objectives like user retention or content virality.
Describe modeling engagement as a single score by assigning weights to each action (e.g., view=1, try=3, share=5, save=4) and summing or averaging. Discuss pros: simplicity, easy to deploy, and interpretability.
Explain training individual models to predict each action (e.g., probability of try, share, save) and then combining them into a final score via another model or rule. Discuss pros: flexibility, ability to capture action-specific nuances, and better handling of sparse actions.
Compare the two approaches on dimensions like data requirements, computational cost, interpretability, ability to handle class imbalance, and ease of updating as new actions emerge.
Suggest a hybrid: use separate models for each action to generate features, then combine them with a learned weighting (e.g., logistic regression or gradient boosting) to produce a final engagement score, balancing flexibility and simplicity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.