Recommender systems are commonplace in the modern digital era and shape our online experiences by recommending movies, goods, and information based on our tastes. However, the Cold Start Problem and Data Sparsity are two key issues that frequently plague these systems.

"Drift" is a concept frequently employed in machine learning to delineate the gradual deterioration in the operational efficacy of a machine learning model over an extended period.

Polyak averaging is named after its inventors Anatoli Juditsky and Arkadii Nemirovski (also known as Polyak and Ruppert) and is a method of model averaging that helps to produce a more robust and accurate final model by combining the information from different iterations.

Multi-Head Attention, a foundational component in modern neural networks, has transformed the way we process and understand sequential and structured data.