  The prevalence of online social network makes it compulsory to study how
social relations affect user choice. However, most existing methods leverage
only first-order social relations, that is, the direct neighbors that are
connected to the target user. The high-order social relations, e.g., the
friends of friends, which very informative to reveal user preference, have been
largely ignored. In this work, we focus on modeling the indirect influence from
the high-order neighbors in social networks to improve the performance of item
recommendation. Distinct from mainstream social recommenders that regularize
the model learning with social relations, we instead propose to directly factor
social relations in the predictive model, aiming at learning better user
embeddings to improve recommendation. To address the challenge that high-order
neighbors increase dramatically with the order size, we propose to recursively
"propagate" embeddings along the social network, effectively injecting the
influence of high-order neighbors into user representation. We conduct
experiments on two real datasets of Yelp and Douban to verify our High-Order
Social Recommender (HOSR) model. Empirical results show that our HOSR
significantly outperforms recent graph regularization-based recommenders NSCR
and IF-BPR+, and graph convolutional network-based social influence prediction
model DeepInf, achieving new state-of-the-arts of the task.

Information Retrieval;Social and Information Networks