Applications of Transformers in Recommendation Models: Capturing Long Term Dependencies Optimization Strategies

Abstract

Transformer architecture has transformed this aspect of recommendation systems by dismantling theunderlying constraints of long-term dependencies and intricate behavioural patterns. The self-attentionmechanism permits dynamic weighting of past interactions according to contextual similarity instead of temporal closeness in order to permit