Most of the recent studies of social recommendation assume that people share
similar preferences with their friends and the online social relations are
helpful in improving traditional recommender systems. However, this assumption
is often untenable as the online social networks are quite sparse and a
majority of users only have a small number of friends. Besides, explicit
friends may not share similar interests because of the randomness in the
process of building social networks. Therefore, discovering a number of
reliable friends for each user plays an important role in advancing social
recommendation. Unlike other studies which focus on extracting valuable
explicit social links, our work pays attention to identifying reliable friends
in both the observed and unobserved social networks. Concretely, in this paper,
we propose an end-to-end social recommendation framework based on Generative
Adversarial Nets (GAN). The framework is composed of two blocks: a generator
that is used to produce friends that can possibly enhance the social
recommendation model, and a discriminator that is responsible for assessing
these generated friends and ranking the items according to both the current
user and her friends' preferences. With the competition between the generator
and the discriminator, our framework can dynamically and adaptively generate
reliable friends who can perfectly predict the current user' preference at a
specific time. As a result, the sparsity and unreliability problems of explicit
social relations can be mitigated and the social recommendation performance is
significantly improved. Experimental studies on real-world datasets demonstrate
the superiority of our framework and verify the positive effects of the
generated reliable friends.

本文提出了一种基于生成式对抗网络（GAN）的端到端社交推荐框架，该框架可以适应地生成可靠的好友，从而改善了显式社交关系的稀疏性和不可靠性问题，提升了社交推荐的性能表现，实验研究表明该框架的优越性和生成可靠好友的积极影响。