  Knowledge graph (KG) plays an increasingly important role in recommender
systems. A recent technical trend is to develop end-to-end models founded on
graph neural networks (GNNs). However, existing GNN-based models are
coarse-grained in relational modeling, failing to (1) identify user-item
relation at a fine-grained level of intents, and (2) exploit relation
dependencies to preserve the semantics of long-range connectivity.
  In this study, we explore intents behind a user-item interaction by using
auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent
Network (KGIN). Technically, we model each intent as an attentive combination
of KG relations, encouraging the independence of different intents for better
model capability and interpretability. Furthermore, we devise a new information
aggregation scheme for GNN, which recursively integrates the relation sequences
of long-range connectivity (i.e., relational paths). This scheme allows us to
distill useful information about user intents and encode them into the
representations of users and items. Experimental results on three benchmark
datasets show that, KGIN achieves significant improvements over the
state-of-the-art methods like KGAT, KGNN-LS, and CKAN. Further analyses show
that KGIN offers interpretable explanations for predictions by identifying
influential intents and relational paths. The implementations are available at
https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.
