Social recommendation has emerged to leverage social connections among users for predicting users’ unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user’s first-order social neighbors’ interests for better user modeling, and failed to model the social influence diffusion process from the global social network structure. Recently, we propose a preliminary work of a neural influence Diff usion Net work (i.e., DiffNet) for social recommendation L. Wu, P. Sun, Y. Fu, R. Hong, X. Wang, and M. Wang, “A neural influence diffusion model for social recommendation,” in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval , 2019, pp. 235–244.. DiffNet models the recursive social diffusion process for each user, such that the influence diffusion hidden in the higher-order social network is captured in the user embedding process. Despite the superior performance of DiffNet, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process in the social network would neglect the latent collaborative interests of users hidden in the user-item interest network. To this end, in this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting both the higher-order user latent interest reflected in the user-item graph and higher-order user influence reflected in the user-user graph for user embedding learning. This is achieved by iteratively aggregating each user’s embedding from three aspects: the user’s previous embedding, the influence aggregation of social neighbors from the social network, and the interest aggregation of item neighbors from the user-item interest network. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from these three aspects. Finally, extensive experimental results on four real-world datasets clearly show the effectiveness of our proposed model. We release the source code at https://github.com/PeiJieSun/diffnet .

中文翻译：

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DiffNet++：用于社交推荐的神经影响和兴趣扩散网络

社交推荐的出现是为了利用用户之间的社交联系来预测用户的未知偏好，这可以缓解基于协同过滤的推荐中的数据稀疏问题。早期的方法依赖于利用每个用户的一阶社交邻居的兴趣来更好地进行用户建模，并且未能从全球社交网络结构中模拟社交影响扩散过程。最近，我们提出了一项神经影响的初步工作差异使用社交推荐的网络（即 DiffNet） L. Wu、P. Sun、Y. Fu、R. Hong、X. Wang 和 M. Wang，“社交推荐的神经影响扩散模型”，载于过程。诠释。ACM SIGIR 会议 水库。开发。信息。Retrieval , 2019, pp. 235–244.. DiffNet 对每个用户的递归社交扩散过程进行建模，从而在用户嵌入过程中捕获隐藏在高阶社交网络中的影响扩散。尽管 DiffNet 具有卓越的性能，但我们认为，由于用户在用户-用户社交网络和用户-项目兴趣网络中都扮演着核心角色，因此仅对社交网络中的影响扩散过程进行建模会忽略隐藏的用户潜在的协作兴趣在用户-项目兴趣网络中。为此，在本文中，我们提出了 DiffNet++，这是一种 DiffNet 的改进算法，在统一的框架中对神经影响扩散和兴趣扩散进行建模。通过将社交推荐重新构建为以社交网络和兴趣网络为输入的异构图，DiffNet++ 通过注入反映在用户项目图中的高阶用户潜在兴趣和反映在用户-用户中的高阶用户影响来推进 DiffNet用户嵌入学习图。这是通过从三个方面迭代聚合每个用户的嵌入来实现的：用户先前的嵌入、来自社交网络的社交邻居的影响聚合以及来自用户-项目兴趣网络的项目邻居的兴趣聚合。此外，我们设计了一个多层次的注意力网络，学习如何从这三个方面专注地聚合用户嵌入。最后，在四个真实世界数据集上的广泛实验结果清楚地表明了我们提出的模型的有效性。我们在以下位置发布源代码https://github.com/PeiJieSun/diffnet .