Tag: cold-start problem

A joint deep learning framework called J-SUP unifies aspect-based sentiment analysis and consumer preference profiling within a shared Transformer architecture ...

A new study reports a quantum-inspired recommendation system combining fuzzy clustering, generative adversarial networks, and a dandelion-based optimizer that achieved ...

Researchers have built a workload-aware prefetching framework that recognizes recurring data hotspots and loads them into the buffer in advance, ...

A new diffusion-based framework extracts transferable preference patterns from historical user data to improve recommendations for users with almost no ...

Researchers have introduced HOFA, a contrastive learning framework that mines triangular motifs in social networks and aligns embedding features to ...

A new semantic-guided graph attention framework improves rating prediction and ranking on Amazon cross-domain recommendation pairs while showing controlled but ...

A new hybrid framework called LLMTransRec combines transformer sequence modeling with large language model embeddings to improve recommendations across sparse ...

Researchers have introduced RPGCN, a relation-preserving graph convolutional network that combats oversmoothing and relational dilution to outperform state-of-the-art baselines across ...

A new hybrid recommender called Meta KG-NCF combines a frozen knowledge graph prior, neural collaborative filtering, and first-order meta-learning to ...

A new study in Administrative Science Quarterly finds that freelance platforms fail to recognise the decades of credentials built by ...