Recommender Systems and Techniques
This cluster of papers focuses on the advancements in recommender system technologies, including collaborative filtering, matrix factorization, deep learning, content-based recommendation, web mining, context-aware recommender systems, neural networks, user modeling, and trust-aware recommender systems. The papers cover various techniques and methodologies for improving recommendation accuracy and addressing challenges such as cold start problems and privacy concerns.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- Matrix Factorization Techniques for Recommender Systems
- Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
- Evaluating collaborative filtering recommender systems
- Amazon.com recommendations: item-to-item collaborative filtering
- The MovieLens Datasets
- Hybrid Recommender Systems: Survey and Experiments
Most recent
- A scalable hybrid recommendation framework for e-commerce using distributed big data processing
- Optimization of Machine Learning–Based Recommendation Systems on E-Commerce Platforms
- Knowledge-Stability-Guided Dual-Graph Contrastive Learning for Recommendation
- Systematic Review Recommendation Engines: Techniques and Their Impact on Customer Engagement
- Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation
- SynRec: Synergistic multi-domain recommendation with frequency-guided expert specialization