  The use of pre-training is an emerging technique to enhance a neural model's
performance, which has been shown to be effective for many neural language
models such as BERT. This technique has also been used to enhance the
performance of recommender systems. In such recommender systems, pre-training
models are used to learn a better initialisation for both users and items.
However, recent existing pre-trained recommender systems tend to only
incorporate the user interaction data at the pre-training stage, making it
difficult to deliver good recommendations, especially when the interaction data
is sparse. To alleviate this common data sparsity issue, we propose to
pre-train the recommendation model not only with the interaction data but also
with other available information such as the social relations among users,
thereby providing the recommender system with a better initialisation compared
with solely relying on the user interaction data. We propose a novel
recommendation model, the Social-aware Gaussian Pre-trained model (SGP), which
encodes the user social relations and interaction data at the pre-training
stage in a Graph Neural Network (GNN). Afterwards, in the subsequent
fine-tuning stage, our SGP model adopts a Gaussian Mixture Model (GMM) to
factorise these pre-trained embeddings for further training, thereby benefiting
the cold-start users from these pre-built social relations. Our extensive
experiments on three public datasets show that, in comparison to 16 competitive
baselines, our SGP model significantly outperforms the best baseline by upto
7.7% in terms of NDCG@10. In addition, we show that SGP permits to effectively
alleviate the cold-start problem, especially when users newly register to the
system through their friends' suggestions.
