  Leveraging the side information associated with entities (i.e. users and
items) to enhance the performance of recommendation systems has been widely
recognized as an important modelling dimension. While many existing approaches
focus on the integration scheme to incorporate entity side information -- by
combining the recommendation loss function with an extra side information-aware
loss -- in this paper, we propose instead a novel pre-training scheme for
leveraging the side information. In particular, we first pre-train a
representation model using the side information of the entities, and then
fine-tune it using an existing general representation-based recommendation
model. Specifically, we propose two pre-training models, named GCN-P and COM-P,
by considering the entities and their relations constructed from side
information as two different types of graphs respectively, to pre-train entity
embeddings. For the GCN-P model, two single-relational graphs are constructed
from all the users' and items' side information respectively, to pre-train
entity representations by using the Graph Convolutional Networks. For the COM-P
model, two multi-relational graphs are constructed to pre-train the entity
representations by using the Composition-based Graph Convolutional Networks. An
extensive evaluation of our pre-training models fine-tuned under four general
representation-based recommender models, i.e. MF, NCF, NGCF and LightGCN, shows
that effectively pre-training embeddings with both the user's and item's side
information can significantly improve these original models in terms of both
effectiveness and stability.
