DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

By Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang. IEEE Transactions on Knowledge and Data Engineering, 2020

350 citations recorded by the source corpus.

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 Diffusion Network (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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/df716e5f5451af2f37b99466c9429eb2916d4c46

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Hongzhi Yin
5. Fuli Feng
6. Meng Wang
7. Yong Li
8. Chen Gao

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Hongzhi Yin
5. Fuli Feng
6. Meng Wang
7. Yong Li
8. Chen Gao

Citations in the corpus

• Graph Neural Networks for Recommender System (2022, background): “Thanks to the powerful modeling ability, GNN has achieved groundbreaking performance and becomes state-of-the-art models in recommender systems [12, 14, 21, 23, 24].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “For instance, DiffNet++ is proposed to jointly model the interest diffusion from user-item bipartite graph and the influence diffusion from the user-user social graph for user modeling in social recommendation, and have achieved state-of-the-art performance [164].”
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, methodology): “• DiffNet++ [40] is the latest GCN-based social recommendation method that models the recursive dynamic social diffusion in both the user and item spaces.”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “DiffNet++ [66] TKDE 2020 √ √ Bipartite + Social graph variant of GAT”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “Then, the methods [43, 61, 82, 116, 133, 155, 197, 198, 201, 217, 225, 226] that model both social network and user interactions with GNNs become the mainstream in GNN-based social recommendation.”
• Diffusion Recommender Model (2023, background): “In addition, some studies on social recommendation consider information diffusion on social networks [47, 48].”