Autoencoder-based contrastive learning for next basket recommendation

Abstract

Next Basket Recommendation (NBR) aims to predict the items in the next basket a user will interact with, based on the user’s basket interaction history. However, data sparsity has been a significant challenge in this area. Contrastive Learning (CL) leverages data augmentation and constructs contrastive losses to enhance the embeddings quality, thus effectively addressing the issue of data sparsity. However, the existing methods rely on adding information to basket embedding or segmenting baskets for contrastive learning, which tend to disrupt the original embedding and have limited applicability in the NBR scenarios with diverse data characteristics. To address the above problems, we propose a novel model called Autoencoder-based Contrastive learning for Next Basket Recommendation (AC-NBR). The proposed method mainly consists of three modules, namely AE-based Basket Augmentation, AE-based Contrastive Learning, and Next-Basket Predictor. In the first module, two different basket augmentation methods are designed to provide sufficient and diverse positive pairs for CL. Specifically, we leverage an encoder-decoder structure with appropriate Gaussian noise to extract key features. This process not only helps mitigate noise interference but also improves the robustness of the embedding representation. In addition, the mean and standard deviation of the embedding representation space are learned separately. Then, Gaussian sampling is performed and the sampled latent representation is reconstructed through the decoder to achieve basket augmentation. This approach preserves core information while enhancing the embedding’s diversity and adaptability. In the second module, based on the two basket augmentations and the initial basket embeddings, three sets of positive pairs are constructed for CL. In the third module, we first encode the optimized basket sequence through a Gated Recurrent Unit (GRU) and then employ two Multi-Layer Perceptrons (MLPs) to predict the items likely to be contained in the next basket, thereby obtaining the final prediction results. The effectiveness of AC-NBR is confirmed through comprehensive experiments on three real-world datasets.