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.

翻译：尽管推荐系统在网络内容平台中扮演着关键角色，挖掘用户兴趣仍是一项重大挑战。现有工作通过利用用户行为（如点击、观看等）来预测用户兴趣，但当用户行为不稳定时，现有解决方案效果欠佳。后者涉及新用户（其任何类型行为都很少）和稀疏用户（具有低频行为）。我们将这两类用户统称为"冷用户"，这类用户在网络内容平台中非常普遍但常被忽视。为解决此问题，我们通过将用户的社交兴趣（如好友关系、关注的博主、兴趣群组等）与行为活动相结合，增强了用户兴趣的表征。因此，本文提出一种名为SocialNet的新算法，采用两阶段方法逐步提取粗粒度和细粒度的社交兴趣。随后，我们的技术将SocialNet的输出与原始用户表征进行拼接，得到融合行为兴趣与社交兴趣的最终用户表征。在腾讯视频推荐系统上的离线实验证明了该方法优于基于行为的基础模型。在线实验也表明，在实际推荐系统中点击量和观看时长均得到显著提升。源代码已开源至https://github.com/Social4Rec/SocialNet。