AI Recommendation Systems

Design user-based and item-based collaborative filtering recommendation systems, including matrix factorization, similarity metrics, and cold-start handling strategies.

Architect content-based recommendation engines using NLP, item feature extraction, TF-IDF, embeddings, and similarity scoring for text, media, and product catalogs.

Design recommendation systems that leverage knowledge graphs, entity relationships, and graph neural networks to improve explainability, accuracy, and semantic understanding.

Design conversational recommendation systems powered by large language models, combining natural language preference elicitation, multi-turn dialogue, and retrieval-augmented item selection.

Design and implement multi-armed bandit and contextual bandit algorithms for recommendation systems to balance exploration and exploitation in real-time personalization.

Design low-latency, high-throughput real-time recommendation serving infrastructures including retrieval, ranking, feature stores, caching layers, and model deployment pipelines.

Improve recommendation quality beyond accuracy by designing diversity, novelty, serendipity, and coverage optimization strategies to reduce filter bubbles and increase user satisfaction.

Solve cold-start problems in recommendation systems for new users and items using onboarding flows, meta-learning, cross-domain transfer, and hybrid initialization strategies.

Audit recommendation systems for popularity bias, exposure fairness, demographic disparities, and feedback loops using established fairness metrics and debiasing techniques.

Build session-based recommendation models using GRU4Rec, SASRec, BERT4Rec, and transformer architectures to predict next items from anonymous or short-horizon interaction sequences.