  Learning informative representations of users and items from the interaction
data is of crucial importance to collaborative filtering (CF). Present
embedding functions exploit user-item relationships to enrich the
representations, evolving from a single user-item instance to the holistic
interaction graph. Nevertheless, they largely model the relationships in a
uniform manner, while neglecting the diversity of user intents on adopting the
items, which could be to pass time, for interest, or shopping for others like
families. Such uniform approach to model user interests easily results in
suboptimal representations, failing to model diverse relationships and
disentangle user intents in representations.
  In this work, we pay special attention to user-item relationships at the
finer granularity of user intents. We hence devise a new model, Disentangled
Graph Collaborative Filtering (DGCF), to disentangle these factors and yield
disentangled representations. Specifically, by modeling a distribution over
intents for each user-item interaction, we iteratively refine the intent-aware
interaction graphs and representations. Meanwhile, we encourage independence of
different intents. This leads to disentangled representations, effectively
distilling information pertinent to each intent. We conduct extensive
experiments on three benchmark datasets, and DGCF achieves significant
improvements over several state-of-the-art models like NGCF, DisenGCN, and
MacridVAE. Further analyses offer insights into the advantages of DGCF on the
disentanglement of user intents and interpretability of representations. Our
codes are available in
https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering.
