Relation-propagation meta-learning on an explicit preference graph for cold-start recommendation.

The cold-start problem has been of great concern in the recommendation domain. To address this problem, meta-learning frameworks have been widely adopted due to their fast adaptation to new tasks. However, existing meta-learning-based methods always use higher-order graph structures to obtain global user preferences but neglect to consider local preferences at different rating levels in a fine-grained manner. Since differences in user ratings on items truly reflect differences in users’ local preferences, we propose a relation-propagation meta-learning on explicit preference graph for cold-start recommendation (RPMLG-Rec) to improve the generalization performance of existing meta-learning-based methods. Specifically, we focus on capturing the relationships between local preferences. First, our RPMLG-Rec approach concatenates different local preferences to form the nodes of local user preference and further constructs an explicit preference graph. Second, the relationships between local preferences, including intraclass commonality and interclass uniqueness, are used to guide the propagation of relationships in the explicit preference graph with graph convolutional networks and produce more distinguishable local preference nodes. Third, the precise global user preference is obtained with an attention mechanism. Finally, prior knowledge is learned based on a set of training tasks and quickly adapted to make recommendations for new tasks, following the optimization-based meta-learning training strategy. To the best of our knowledge, this is the first time that the relationships between local user preference nodes have been explicitly considered in cold-start recommendation. In addition, we conducted extensive experiments on two real-world datasets, and the experimental results demonstrate the effectiveness of our proposed framework.