Social Boosted Recommendation With Folded Bipartite Network Embedding
Social Boosted Recommendation With Folded Bipartite Network Embedding
复制标题
DOI：
10.1109/tkde.2020.2982878
复制
发表时间：
2020-03
影响因子：
8.9
通讯作者：
Hongxu Chen;Hongzhi Yin;Tong Chen;Weiqing Wang;Xue Li;Xia Hu
中科院分区：
文献类型：
--
作者：
Hongxu Chen;Hongzhi Yin;Tong Chen;Weiqing Wang;Xue Li;Xia Hu
随着在线社交平台的普及，社交推荐已经成为一个很有前途的方向，利用用户之间的社交网络来提高推荐性能。然而，用户之间可用的社会关系通常是非常稀疏和嘈杂的，这可能会导致较差的推荐性能。为了缓解这个问题，本文新颖地利用隐含的高阶社会影响和用户之间的依赖关系，以提高社会推荐。在本文中，我们提出了一种新的嵌入方法，一般的二分图，它定义了显式关系之间的类间消息传递和隐式高阶关系之间的类内消息传递通过一种新的顺序建模范式。受自我注意力序列建模的最新进展的启发，所提出的模型具有隐式用户-用户关系的自我注意表示学习机制。此外，本文还探讨了社会推荐问题的归纳嵌入学习，以提高冷启动环境下的推荐性能。所提出的用于社会推荐的归纳学习范例能够为那些冷启动用户和项目（在训练期间看不见）嵌入推理，只要它们链接到原始网络中的现有节点。在真实数据集上的大量实验证明了该方法的优越性，并表明用户之间的高阶隐式关系有利于提高社会推荐。
With the prevalence of online social platforms, social recommendation has emerged as a promising direction that leverages the social network among users to enhance recommendation performance. However, the available social relations among users are usually extremely sparse and noisy, which may lead to inferior recommendation performance. To alleviate this problem, this paper novelly exploits the implicit higher-order social influence and dependencies among users to enhance social recommendation. In this paper, we propose a novel embedding method for general bipartite graphs, which defines inter-class message passing between explicit relations and intra-class message passing between implicit higher-order relations via a novel sequential modelling paradigm. Inspired by recent advances in self-attention-based sequential modelling, the proposed model features a self-attentive representation learning mechanism for implicit user-user relations. Moreover, this paper also explores the inductive embedding learning for social recommendation problems to improve the recommendation performance in cold-start settings. The proposed inductive learning paradigm for social recommendation enables embedding inference for those cold-start users and items (unseen during training) as long as they are linked to existing nodes in the original network. Extensive experiments on real-world datasets demonstrate the superiority of our method and suggest that higher-order implicit relationship among users is beneficial to improving social recommendation.