Matrix Factorization Techniques for Recommender Systems

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As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.

Last synced 2026-08-22 14:19 UTC

• Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation

  Algorithms 2026 · 0 citations

• A scalable hybrid recommendation framework for e-commerce using distributed big data processing

  Discover Computing 2026 · 0 citations

• A Sentiment-Driven Deep Learning System for Hospital Recommendation

  Electronics 2026 · 0 citations

• Cross-Modally Aligned and Temporally Gated Mixture of Experts for Multimodal Sequential Recommendation

  Big Data and Cognitive Computing 2026 · 0 citations

• From scoring to workflow: A survey of agentic recommender systems

  Information Processing & Management 2026 · 0 citations

• Group-Aware Fair and Diverse Recommendation via Dual-View Preference Modeling and Rank-Sensitive Re-Ranking: Evidence from Real Travel Groups

  Information 2026 · 0 citations

• Encoder Language Models for Zero-Shot Recommender Systems: Cross-Domain and Cross-Lingual Evaluation

  Computers 2026 · 0 citations

• Hybrid Graph Retrieval-Augmented Language Agents for Collaborative Recommendation

  AI 2026 · 0 citations

• Target item identification for injection attacks in recommender systems via graph-enhanced user representations

  Information Processing & Management 2026 · 0 citations

• MEFCL: Reliability-aware multi-view edge-filtered contrastive learning for graph recommendation

  Pattern Recognition 2026 · 0 citations