NIE-GCN: Neighbor Item Embedding-Aware Graph Convolutional Network for Recommendation

Graph convolutional networks (GCNs) have been widely used to learn high-quality representations (a.k.a. embeddings) from multiorder neighbors in recommendation tasks. However, many existing GCN-based methods learn user and item embeddings in the user–item interaction bipartite graph indistinguishably, ignoring the inherent heterogeneity of the bipartite graph, i.e., users and items are two distinct types of entities. This article explores in depth the high-order connections for user and his (her) neighbor items. We propose an innovative model, neighbor item embedding-aware graph convolutional network (NIE-GCN). As opposed to previous GCN-based approaches, NIE-GCN employs a novel dual user aggregation (DUA) scheme and a neighbor-aware attention mechanism to construct user embeddings and distinguish the contribution of different neighbor nodes. In addition, we propagate information in an alternating manner to eliminate the effects of heterogeneity of user–item interaction bipartite graph. According to detailed experiments on three large-scale datasets, the proposed NIE-GCN significantly outperforms state-of-the-art approaches on the Top-
$N$
recommendation task while reducing model parameters by about half. Further analyses show the effectiveness and rationality of DUA and neighbor-aware attention mechanism.