  Despite recommender systems play a key role in network content platforms,
mining the user's interests is still a significant challenge. Existing works
predict the user interest by utilizing user behaviors, i.e., clicks, views,
etc., but current solutions are ineffective when users perform unsettled
activities. The latter ones involve new users, which have few activities of any
kind, and sparse users who have low-frequency behaviors. We uniformly describe
both these user-types as "cold users", which are very common but often
neglected in network content platforms. To address this issue, we enhance the
representation of the user interest by combining his social interest, e.g.,
friendship, following bloggers, interest groups, etc., with the activity
behaviors. Thus, in this work, we present a novel algorithm entitled SocialNet,
which adopts a two-stage method to progressively extract the coarse-grained and
fine-grained social interest. Our technique then concatenates SocialNet's
output with the original user representation to get the final user
representation that combines behavior interests and social interests. Offline
experiments on Tencent video's recommender system demonstrate the superiority
over the baseline behavior-based model. The online experiment also shows a
significant performance improvement in clicks and view time in the real-world
recommendation system. The source code is available at
https://github.com/Social4Rec/SocialNet.
