Auxiliary Item Relationship

Auxiliary item relationships represent connections between items beyond simple sequential interactions, enriching data used in various machine learning tasks. Current research focuses on incorporating these relationships, often using multiple auxiliary relations simultaneously or leveraging them in few-shot learning scenarios, with model architectures like multi-relational transformers being employed. This research improves the accuracy and efficiency of tasks such as knowledge graph entity typing and sequential recommendation, particularly addressing challenges like cold-start problems and fairness in predictions, ultimately leading to more robust and reliable machine learning systems.