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）预测用户兴趣，但当前解决方案在用户活动不稳定时效果不佳。后者涉及新用户（其各类活动极少）和稀疏用户（行为频率低）。我们将这两种用户类型统一描述为“冷用户”，这类用户在网络内容平台中极为常见却常被忽视。为解决此问题，我们通过融合用户的社交兴趣（如好友关系、关注博主、兴趣小组等）与活动行为来增强用户兴趣表示。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与细粒度的社交兴趣。该技术随后将SocialNet的输出与原始用户表示拼接，得到融合行为兴趣与社交兴趣的最终用户表示。在腾讯视频推荐系统上的离线实验表明，该模型优于基于行为的基础模型。在线实验同样显示，在真实推荐系统中点击率与观看时长均显著提升。源代码已开源：https://github.com/Social4Rec/SocialNet。