A Neural Influence Diffusion Model for Social Recommendation
DiffNet models recursive social influence diffusion, improving recommendation accuracy by over 13% on real datasets.
Key Findings
Methodology
DiffNet employs a layer-wise influence diffusion structure to simulate recursive social influence propagation. It begins with initial user embeddings fused with features and latent preferences, then iteratively updates these embeddings through multiple diffusion layers. Each layer aggregates influences from trusted users via pooling (average or max), combined with non-linear transformations, to model dynamic interest evolution. The model operates without requiring explicit user or item attributes, relying solely on social network structure. Training utilizes pairwise BPR loss with stochastic gradient descent, ensuring efficiency. The architecture integrates seamlessly with traditional collaborative filtering methods, enhancing their capacity to capture interest dynamics.
Key Results
- On Yelp and Flickr datasets, DiffNet achieved over 13.5% and 15.5% improvements respectively in Top-10 recommendation accuracy compared to best baselines like TrustSVD and GCN-based models. The optimal performance was observed at diffusion depth K=2, validating the effectiveness of recursive influence modeling.
- Ablation studies confirmed that the influence diffusion layers significantly boost user embedding quality, leading to higher recommendation precision. The model demonstrated robustness in sparse and attribute-lacking scenarios, outperforming static neighbor-based approaches.
- Efficiency analysis showed that DiffNet's parameter sharing and shallow influence layers reduce training time and memory costs relative to deep graph neural networks, making it suitable for large-scale deployment.
Significance
This work advances social recommendation by explicitly modeling the recursive influence process, capturing the temporal evolution of user interests. It addresses the limitations of static neighbor models, providing a more realistic and flexible framework. The approach enhances recommendation accuracy while maintaining computational efficiency, offering a practical solution for real-world systems with sparse or incomplete data. Its generality allows application across various domains, fostering further research into dynamic influence modeling in recommendation systems.
Technical Contribution
DiffNet introduces a novel layer-wise influence diffusion mechanism that recursively propagates social influence, integrating pooling and non-linear transformations. It bridges the gap between static neighbor models and dynamic influence processes, enabling end-to-end training. The model's design reduces complexity via parameter sharing, improves expressiveness through multi-layer diffusion, and maintains compatibility with classical CF methods, representing a significant step forward in influence-aware recommendation modeling.
Novelty
This is the first work to incorporate a recursive influence diffusion process into deep neural recommendation models, explicitly simulating how influence propagates through social networks over multiple steps. Unlike prior static neighbor-based approaches, DiffNet captures the temporal and structural dynamics of influence, providing a more faithful representation of interest evolution. Its flexible architecture works without user or item attributes, broadening applicability and outperforming existing methods.
Limitations
- The model heavily depends on the social network structure; sparse or noisy networks may reduce effectiveness. Incorporating content features could mitigate this but was not explored here.
- Multiple diffusion layers increase computational overhead, especially in large networks; although sharing parameters alleviates this, scalability remains a concern.
- The current framework does not explicitly model temporal interest shifts; future work should integrate time-aware mechanisms to better reflect interest dynamics.
Future Work
Future research will focus on integrating content and temporal data to enhance robustness and adaptivity. Exploring multi-modal features, extending influence modeling to heterogeneous networks, and optimizing scalability for industrial-scale deployment are promising directions. Additionally, developing explainability mechanisms for influence pathways could improve interpretability and user trust.
AI Executive Summary
In recent years, social networks have become a vital source of information for personalized recommendation systems. Traditional collaborative filtering methods, while effective, struggle with data sparsity and static assumptions about user preferences. Static neighbor-based social recommendation models, such as TrustSVD, assume influence is limited to immediate neighbors, neglecting the recursive and dynamic nature of influence propagation in real social networks. This limitation hampers their ability to accurately model interest evolution over time.
To address this, the authors propose DiffNet, a deep neural network that explicitly simulates the recursive influence diffusion process. The core innovation lies in a layer-wise diffusion architecture, where each layer aggregates influence from trusted users using pooling operations, then combines this with the current user embedding through non-linear transformations. This recursive process continues for a predefined number of steps, capturing the evolving interests of users as influence propagates through the social network.
Experimental results on Yelp and Flickr datasets demonstrate that DiffNet significantly outperforms state-of-the-art baselines, with improvements exceeding 13% in top-10 recommendation accuracy. The model's design ensures computational efficiency, thanks to parameter sharing and shallow diffusion layers, making it suitable for large-scale deployment. Ablation studies confirm that the influence diffusion mechanism is critical for performance gains, validating the importance of modeling recursive social influence.
The broader impact of this work is substantial. By moving beyond static neighbor assumptions, DiffNet offers a more realistic and flexible framework for social recommendation, capable of adapting to various data availability scenarios. Its ability to model interest dynamics opens new avenues for personalized content delivery, social influence analysis, and user engagement enhancement. The research paves the way for future integration of content, temporal, and multi-modal data, further enriching recommendation systems and deepening our understanding of social influence mechanisms.
Deep Analysis
Background
推荐系统在电子商务和内容平台中扮演核心角色。传统方法如矩阵分解和协同过滤（CF）在利用用户行为数据方面取得显著成功，但面对数据稀疏问题时表现不佳。随着社交网络的兴起，社交推荐逐渐成为研究热点，利用用户关系缓解数据不足，基于社交影响的模型（如TrustSVD、GCN）应运而生。然而，这些模型多为静态邻居假设，未考虑影响的递归传播机制，限制了其对兴趣动态变化的捕捉能力。近年来，图神经网络（GNN）和影响传播模型的发展，为模拟复杂的影响机制提供了新工具，但在推荐中的应用仍处于探索阶段。
Core Problem
核心问题在于，现有社交推荐模型多采用静态邻居关系，忽略了影响在社交网络中的递归传播过程。这导致模型无法动态捕捉用户兴趣随时间变化的特性，影响推荐效果。如何有效模拟影响的多层次递归扩散，反映兴趣的演变，是提升推荐性能的关键。特别是在用户关系稀疏或缺少丰富内容信息的场景下，模型的适应性和效率成为亟待解决的问题。
Innovation
本研究提出DiffNet模型，具有以下创新点：
- �� 层级影响扩散机制，模拟递归影响传播，动态演变用户潜在偏好；
- �� 采用多层池化操作（平均或最大池化）整合受信任用户的影响信息；
- �� 无需用户或物品的显式属性，纯粹依赖社交关系进行影响建模；
- �� 结合深度学习与传统协同过滤，提升模型表达能力和效率。该模型通过端到端训练，显著优于静态邻居模型，捕获兴趣的复杂演变过程。
Methodology
- �� 输入：用户-物品交互矩阵、社交关系图、用户和物品特征；
- �� 嵌入层：学习用户和物品的潜在表示（如P、Q矩阵）；
- �� 融合层：将潜在表示与特征融合，生成初始兴趣向量（h0a、v_i）；
- �� 影响扩散层：
- ��– 多层结构，每层接收上一层输出；
- ��– 每层通过池化（平均或最大）整合受信任用户影响，得到影响向量hk+1Sa；
- ��– 结合非线性变换，更新用户表示hk+1a；
- �� 递归多层传播，直到预设深度K，得到最终用户兴趣表示hKa；
- �� 预测层：将用户兴趣向量与物品向量内积，输出偏好预测值；
- �� 损失函数：采用pairwise BPR，利用正负样本优化模型参数。
Experiments
在Yelp和Flickr两个公开数据集上，评估模型在Top-10推荐任务中的性能。比较基线包括TrustSVD、GCN推荐等。超参数设置包括传播深度K（主要取值为2）、学习率、正则化系数。通过消融实验验证影响传播层的重要性，分析不同深度对性能的影响。训练采用Adam优化器，批次大小合理，确保训练效率。模型在不同稀疏度和无属性场景下表现优异，验证了其广泛适用性。
Results
DiffNet在Yelp和Flickr数据集上的Top-10推荐性能分别超越最优基线13.5%和15.5%。多层传播（K=2）效果最佳，验证了递归模拟影响的有效性。消融实验显示，影响扩散层显著提升用户嵌入质量，带来更高的准确率。模型在稀疏和无属性场景下依然表现优越，训练时间和存储成本低于深度图神经网络，验证其实用性。
Applications
该模型适用于社交平台、电子商务、内容推荐等场景，尤其在用户关系稀疏或缺少丰富属性信息时表现出色。可部署于大规模推荐系统中，提升个性化推荐效果，降低系统复杂度。未来结合内容和时间信息，将进一步增强模型的动态适应能力，拓展应用范围。
Limitations & Outlook
模型依赖社交关系网络，网络稀疏或不完整时效果受限。多层影响传播增加计算成本，需优化算法。未考虑兴趣的时间动态变化，未来应引入时间感知机制以更好捕捉兴趣演变。
Plain Language Accessible to non-experts
想象你在一个大家庭中，每个人都喜欢不同的食物。有时候，你听到邻居说：“这个菜很好吃”，你也会开始喜欢它。可是，邻居的喜好会随着时间变化，可能今天喜欢，明天又不喜欢。家庭成员的喜好会互相影响，像水波一样在家庭中传播。这不是一次性传递，而是不断递归地影响彼此，直到每个人的偏好稳定下来。这个模型就像模拟水波扩散的过程，逐步理解每个人的兴趣变化，从而推荐你可能喜欢的菜肴。它比以前只看邻居的偏好更聪明，因为它考虑了影响的递归传播，能更真实地反映兴趣的动态变化。
ELI14 Explained like you're 14
想象你和朋友们在学校玩传话游戏。一个人说一句话，然后传给下一个人，大家可能会把话传得更远、更变味。这个游戏就像社交网络中的影响传播：一个人的想法会通过朋友传给更多人，逐渐影响整个圈子。以前的模型就像只看邻近的朋友，觉得他们的想法差不多，但其实，影响像水波一样，会慢慢扩散到更远的朋友，甚至影响每个人的兴趣变化。这个新模型就像模拟水波一样，逐层传播影响，帮我们更好理解每个人的兴趣是怎么变的。这样，我们可以更精准地推荐你喜欢的电影或商品，就像知道谁的喜欢会影响谁一样，效果更自然、更贴心！
Abstract
Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering(CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user~(item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model, with more than 13% performance improvements over the best baselines.