Start by clarifying the business goal and defining conversion precisely, then walk through the ML lifecycle: data collection, feature engineering, model selection, loss function choice, and evaluation. Emphasize how the system integrates with Spotify's product and experimentation framework, and discuss trade-offs between precision and recall given the business context.
Pro tip: Frame the problem as a ranking task rather than binary classification, since the goal is to prioritize users for targeted interventions. Also, mention the importance of defining a clear prediction horizon (e.g., 30 days) and handling class imbalance with techniques like negative downsampling or focal loss.
Define conversion (e.g., subscription within 30 days), identify the prediction unit (user), and specify the prediction horizon. Clarify business constraints like intervention cost and desired precision.
Collect historical user data: demographics, engagement metrics (listening time, skips, playlist creation), and contextual signals. Engineer features like trend of engagement, recency, frequency, and interactions with premium prompts.
Choose models suitable for tabular data with temporal aspects: gradient boosted trees (XGBoost, LightGBM) or deep learning with embeddings for sequential behavior. Consider ranking models like LambdaMART if prioritizing users.
For binary conversion, use log loss with class weights or focal loss to handle imbalance. If ranking, use pairwise or listwise losses (e.g., LambdaRank) to optimize top-k precision.
Evaluate offline with AUC-ROC, PR-AUC, and precision@k. Validate online via A/B test measuring conversion lift and ROI. Deploy with monitoring for drift and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.