Collaborative Filtering & Matrix Factorization
User-item collaborative models (ALS, matrix factorization, neural CF) for personalized recommendations from implicit and explicit feedback signals.
CollaborativeMatrix
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Build recommendation engines and personalization at scale — from offline evaluation and A/B testing to real-time, production-ready serving.
User-item collaborative models (ALS, matrix factorization, neural CF) for personalized recommendations from implicit and explicit feedback signals.
Item profiling and content embeddings (text, image, metadata) to recommend similar items and cold-start solutions using semantic similarity.
Combine collaborative and content signals, use session-based approaches and graph embeddings for discovery and session personalization.
Low-latency serving with feature stores, online updates, Kafka/streaming pipelines and cache strategies for real-time personalization at scale.
Personalization pipelines, offline metrics (HR, NDCG) and online experiments (A/B, bandits) to measure business impact and fine-tune strategies.
Learning-to-rank, re-ranking for diversity/relevance trade-offs and techniques to detect and mitigate bias in recommendations.