Learning Intents behind Interactions with Knowledge Graph for Recommendation

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 [41], KGNN-LS [38], and CKAN [47].
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.