Microsoft ML System Design: Local Sports Team Recommender

Overview

You are asked to design a recommendation system for a mobile app that helps users discover and follow local sports teams (amateur clubs, community leagues, school teams). The system should use user preferences, location, and behavioral signals to generate personalized team suggestions at scale, with low-latency responses and near-real-time updates.

High-level flow

1. Data collection: ingest user profiles (location, sports interests, availability), interaction logs (clicks, follows, joins), and team data (location, sport, schedule, ratings).
2. Offline pipelines: preprocess data, build item/team embeddings, train candidate-generation models (CF, content-based, locality heuristics) and ranking models (learning-to-rank).
3. Online serving: fetch candidates from a cache/ANN store, compute fresh features from a feature store, run a lightweight ranker, apply filters (distance, skill-level), and serve within 100–200 ms.
4. Feedback loop: collect implicit/explicit feedback to retrain models and adjust personalization.

Key considerations and components

You should discuss candidate generation vs. ranking separation, a feature store (for real-time and batch features), approximate nearest neighbors (for embeddings), streaming ingestion (Kafka), model deployment (online models vs. offline batch scoring), caching strategies, and AB testing. Address cold-start for new users/teams using content-based signals and onboarding preferences.

Skill signals interviewers expect

You’ll need to demonstrate knowledge of recommender algorithms (collaborative filtering, content-based, hybrid approaches), system architecture for low-latency serving, design for scale and availability, feature engineering and freshness, evaluation metrics (CTR, precision@k, recall@k), and trade-offs between relevance, fairness, and privacy.