Most existing studies that employ social-trust information to solve the data sparsity issue in recommender systems assume that socially connected users have equal influence on each other. However, this assumption does not hold in practice since users and their friends may not have similar interests because social connections are multifaceted and exhibit heterogeneous strengths in different scenarios. Therefore, estimating the diverse levels of influence among entities (users/items/social connections) is very important in advancing social recommender systems. Towards this goal, we propose a new model named Social-Trust-Aware Variational Recommendation (SOAP-VAE). Particularly, SOAP-VAE leverages graph attention network techniques to capture the varying levels of influence and the complex interaction patterns among all the entities collectively and holistically. In doing so, heterogeneity among entities is obtained seamlessly. Consequently, we generate social-trust-aware item embedding representations in which the right level of influence has been integrated. Next, based on these rich social-trust-aware item representations, we formulate the first-ever social-trust-aware prior in literature. Unlike priors utilized in earlier VAE-based recommendation models, this novel prior aids in dealing with the issue of posterior-collapse and can effectively capture the uncertainty of latent space. In effect, the model produces better latent representations, which significantly alleviates the data sparsity issue. Finally, we empirically show that SOAP-VAE outperforms several state-of-the-art baselines on three real-world data sets.

中文翻译：

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社会信任感知的变分推荐

大多数现有的利用社会信任信息来解决推荐系统中的数据稀疏问题的研究都假设具有社会联系的用户彼此之间具有同等的影响力。然而，这个假设在实践中并不成立，因为用户和他们的朋友可能没有相似的兴趣，因为社交联系是多方面的，并且在不同的场景中表现出不同的优势。因此，估计实体（用户/物品/社交联系）之间不同程度的影响对于推进社交推荐系统非常重要。为实现这一目标，我们提出了一个名为 Social-Trust-Aware Variational Recommendation (SOAP-VAE) 的新模型。特别，SOAP-VAE 利用图注意力网络技术从整体上和整体上捕获所有实体之间不同程度的影响和复杂的交互模式。这样做可以无缝地获得实体之间的异质性。因此，我们生成了社会信任感知项目嵌入表示，其中已整合了正确的影响水平。接下来，基于这些丰富的社会信任感知项目表示，我们制定了文学史上第一个社会信任意识。与早期基于 VAE 的推荐模型中使用的先验不同，这种新颖的先验有助于处理后塌陷问题，并且可以有效地捕捉潜在空间的不确定性。实际上，该模型产生了更好的潜在表示，这显着缓解了数据稀疏问题。最后，我们凭经验证明 SOAP-VAE 在三个真实数据集上优于几个最先进的基线。