Causal Meta-learning with Multi-view Graphs for Cold-start Recommendation

Cold-start recommendation is a well-known problem in practical application scenarios. Generating reliable recommendations can be challenging when interactions are typically sparse. To mitigate the cold-start problem, some methods incorporate auxiliary information about users and items, and others adopt meta-learning to improve recommendation accuracy. However, these approaches overlook the fact that items are interdependent and likely to be related or similar. Moreover, user preference distributions in the meta-training and meta-testing phases are different in the cold-start scenario. To address these problems, we present a novel strategy called Causal Meta-learning with Multi-view Graphs (CausalMMG). Specifically, we first construct multi-view item-item graphs to explore the correlations and similarities between items from multiple perspectives. A multi-view item representer is then used to learn item representations, exploiting graph convolution neural networks to capture the structure of these different item–item graphs. We then resort to the structural causal models of causal inference and further develop a causality-enhanced bi-level adaptive meta-learner to eliminate bias caused by the different distributions of user preferences. Moreover, the meta-learner learns the user preferences for items in different orders through hierarchical and task-level adaptations. Finally, we evaluate CausalMMG on several real-world datasets, demonstrating its effectiveness in various scenarios. The results show that the proposed CausalMMG is significantly superior to competitive baseline methods for cold-start recommendation on all datasets, highlighting the importance of incorporating the multiple relationships between items and modeling different user preference distributions in recommender systems.