Advancing Multibehavior Recommendation With Dual-Mode Augmented Contrastive Learning

Multibehavioral recommender systems improve the prediction accuracy of target behaviors (e.g., purchase) by integrating auxiliary user behaviors (e.g., page view), but often face the challenge of sparse data on target behaviors. Although contrastive learning can effectively alleviate this problem, existing models still have limitations: they reduce behavioral relationships to linear combinations, ignoring users’ unique behavioral combination modes for different items; meanwhile, when constructing contrastive views, they fail to adequately consider the data imbalance between auxiliary and target behaviors, resulting in learned features biased toward auxiliary behaviors. Consequently, we introduced a multibehavioral recommendation model founded on dual mode contrast learning (DMCL). DMCL innovatively defines behavior combination modes and generates interaction graphs from two perspectives: behavior combination and cascading, and designs a specialized graph encoder for each graph to learn node embedding. By introducing adversarial constraints and contrastive learning, DMCL enhances the complementary nature of node embeddings, and at the same time utilizes interaction graphs in different modes to construct a contrast view, which makes full use of the limited interaction information of the target behavior, and effectively mitigates the feature bias problem. Finally, DMCL combines multilayer perceptron to predict recommendation scores. Our comprehensive experiments on three real datasets demonstrate significant improvements in Recall and NDCG metrics, achieving increases of 2.4–10.1% and 2.6–10.3%, respectively, over current state-of-the-art methods.