BERT4Rec is a bidirectional sequential recommendation model that uses deep self-attention mechanisms to capture user behavior sequences. Unlike previous left-to-right unidirectional models, BERT4Rec employs a bidirectional approach to model user behavior, allowing each item in the sequence to fuse information from both left and right contexts. To avoid information leakage and efficiently train the bidirectional model, the Cloze objective is used, where items in the sequence are randomly masked and predicted based on their surrounding context. This approach enables the model to learn bidirectional representations for sequential recommendation. Extensive experiments on four benchmark datasets show that BERT4Rec outperforms various state-of-the-art sequential models consistently. The model's bidirectional architecture and Cloze objective contribute to its superior performance in recommendation tasks. The paper also discusses related works, model architecture, and evaluation results, demonstrating the effectiveness of BERT4Rec in sequential recommendation.BERT4Rec is a bidirectional sequential recommendation model that uses deep self-attention mechanisms to capture user behavior sequences. Unlike previous left-to-right unidirectional models, BERT4Rec employs a bidirectional approach to model user behavior, allowing each item in the sequence to fuse information from both left and right contexts. To avoid information leakage and efficiently train the bidirectional model, the Cloze objective is used, where items in the sequence are randomly masked and predicted based on their surrounding context. This approach enables the model to learn bidirectional representations for sequential recommendation. Extensive experiments on four benchmark datasets show that BERT4Rec outperforms various state-of-the-art sequential models consistently. The model's bidirectional architecture and Cloze objective contribute to its superior performance in recommendation tasks. The paper also discusses related works, model architecture, and evaluation results, demonstrating the effectiveness of BERT4Rec in sequential recommendation.