Attribute Graph Neural Networks for Strict Cold Start Recommendation: Extended Abstract

Recently, deep learning based methods, especially graph neural network (GNN), have made impressive progress on rating prediction problem in recommender systems. However, the performance of existing methods drops quickly in the cold start scenario. More importantly, such methods are unable to learn the preference embedding of a strict cold start user/item since there is no interaction for this user/item. In this work, we develop a novel framework Attribute Graph Neural Networks (AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. AGNN can produce the preference embedding for a strict cold user/item by learning on the distribution of attributes with an extended variational auto-encoder (eVAE) structure. It also contains a new graph neural network variant (gated-GNN) to effectively aggregate various attributes of different dimensions in a neighborhood. Empirical results demonstrate that AGNN achieves the new state-of-the-art performance.