AEN ‐ GCL : Aspect‐Guided Embedding and Negative Sampling With Graph Contrastive Learning for Recommendation

Graph neural networks enhance collaborative signals through multi‐hop message passing, achieving promising results in recommender systems. Nevertheless, several challenges still remain. Most existing methods rely solely on user‐item interactions, which are insufficient to capture users' fine‐grained preferences. Although some approaches exploit aspect information to alleviate this issue, they typically treat aspects as auxiliary information and fail to explicitly integrate them into user (item) embeddings. Additionally, these methods generally treat all unobserved items as negative samples and employ a uniform sampling strategy. However, due to the inherent popularity bias of items, such a negative sampling strategy is suboptimal for effective model training. To address the above issues, we propose a novel method, AEN‐GCL, which leverages aspect information to help construct the initial embeddings of users (items) and guide negative sampling, while incorporating a self‐supervised auxiliary learning task. Specifically, we first employ a pre‐trained Sentence‐BERT to compute the similarities between reviews and predefined seed sentences of each aspect. The similarities derived from the reviews of each user (item) are aggregated to form an aspect representation, which is combined with category information to construct the initial embedding. Next, we compute the aspect‐level similarity between the user, the positive item and candidate negatives generated by an adaptive category‐based strategy. The user‐aware and item‐aware similarities are jointly used to guide negative sampling. In addition, to alleviate sparse supervision signals, we incorporate edge‐dropout‐based graph contrastive learning to enhance model training. Comprehensive experiments on three public datasets demonstrate that the proposed AEN‐GCL achieves competitive performance compared with state‐of‐the‐art methods.