Fusion of Masked Autoencoder for Adaptive Augmentation Sequential Recommendation

Abstract

In order to address the issue of poor-quality contrast views generated by contrastive learning methods in sequential recommendation tasks, a model called GATSR, which is based on graph attention networks for sequential recommendation, is proposed. Firstly, a global item-item transition graph is created based on all user interaction sequences, combining sequential patterns with global collaborative patterns to provide global context for the item representation. Then, an adaptive graph augmentation module is designed to extract important self-supervised signals based on an adaptive sampling strategy, learning more accurate item representations and effectively avoiding the interference of noise signals. Subsequently, the masked autoencoder module employs re-masking technology to mask to highly semantically related masked items again, enabling the encoder to learn higher-level item representations and achieving the reasonable reconstruction of masked items. Finally, the sequential recommender module integrates position information, global context, and the personalized user interaction sequence to obtain the final item representation and predict the user's future possible interaction items based on the representation, thereby providing more reliable recommendation results for users. Experimental results on the Books, Toys, and Retailrocket datasets show that the recommendation accuracy of the proposed model is superior to the most advanced baseline algorithms in terms of hit ratio (HR) and normalized discounted cumulative gain (NDCG) metrics. For example, it improves by 4.59% on the HR@5 metric and 8.89% on the NDCG@5 metric compared with the most advanced baseline.

Keywords

• sequential recommendation; adaptive data augmentation; autoencoder; self-supervised learning