Modeling user interest accurately is crucial to recommendation systems. Existing works capture user interest from historical behaviors. Due to the sparsity and noise in user behavior data, behavior based models learn incomplete and sometimes inaccurate preference patterns and easily suffer from the cold user problem. To address these issues, we propose a social graph enhanced framework for behavior based models, namely Social4Rec. The social graph involving multiple relation types is extracted to find users with similar interests. It is challenging due to the trivial and sparse relations in social graph. Specifically, we first propose a Cluster-Calibrate-Merge network (CCM) to discover interest groups satisfying three properties: intrinsic self-organizing patterns through cluster layer, robustness to sparse relations through knowledge distillation of calibrator layer. We then use the averaged user interest representation within each group from CCM to complete each user behavior embedding and obtain relation specific interest aware embedding. To alleviate the trivial relation problem, relation specific interest aware embedding are aggregated among relation types through attention mechanism to obtain the interest aware social embedding for each user. It is combined with user behavior embedding to derive the matching score between the user and item. Both offline and online experiments on Tencent Video, which is one of the biggest video recommendation platforms with nearly one billion users in China, demonstrate the superiority of our work, especially for cold users. The codes are available on https://github.com/Social4Rec/SocialN

翻译：准确建模用户兴趣对推荐系统至关重要。现有工作通过历史行为捕获用户兴趣，但由于用户行为数据的稀疏性和噪声，基于行为的模型学习到的偏好模式不完整且有时不准确，且易受冷启动用户问题影响。为解决这些问题，我们提出一种面向行为模型的社交图增强框架——Social4Rec。该框架提取包含多种关系类型的社交图，以发现具有相似兴趣的用户，但社交图中琐碎且稀疏的关系带来了挑战。具体而言，我们首先提出一种聚类-校准-合并网络（CCM），通过聚类层发现满足内在自组织模式的三类兴趣群体，并通过校准层的知识蒸馏实现稀疏关系的鲁棒性；然后利用CCM中每个群体内平均用户兴趣表示来补全每个用户行为嵌入，得到关系特定的兴趣感知嵌入；为缓解琐碎关系问题，通过注意力机制聚合关系类型中的关系特定兴趣感知嵌入，获得每个用户的兴趣感知社交嵌入，并将其与用户行为嵌入结合以计算用户与物品的匹配分数。在拥有近十亿中国用户的腾讯视频（中国最大视频推荐平台之一）上进行的离线和在线实验表明，本工作的优越性尤其在冷启动用户场景中更为显著。代码已开源至https://github.com/Social4Rec/SocialN。