  As a pivotal tool to alleviate the information overload problem, recommender
systems aim to predict user's preferred items from millions of candidates by
analyzing observed user-item relations. As for alleviating the sparsity and
cold start problems encountered by recommender systems, researchers resort to
employing side information or knowledge in recommendation as a strategy for
uncovering hidden (indirect) user-item relations, aiming to enrich observed
information (or data) for recommendation. However, in the face of the high
complexity and large scale of side information and knowledge, this strategy
relies for efficient implementation on the scalability of recommendation
models. Not until after the prevalence of machine learning did graph embedding
techniques be a concentration, which can efficiently utilize complex and
large-scale data. In light of that, equipping recommender systems with graph
embedding techniques has been widely studied these years, appearing to
outperform conventional recommendation implemented directly based on graph
topological analysis. As the focus, this article retrospects graph
embedding-based recommendation from embedding techniques for bipartite graphs,
general graphs and knowledge graphs, and proposes a general design pipeline of
that. In addition, after comparing several representative graph embedding-based
recommendation models with the most common-used conventional recommendation
models on simulations, this article manifests that the conventional models can
overall outperform the graph embedding-based ones in predicting implicit
user-item interactions, revealing the comparative weakness of graph
embedding-based recommendation in these tasks. To foster future research, this
article proposes suggestions on making a trade-off between graph
embedding-based recommendation and conventional recommendation in different
tasks, and puts forward open questions.
