A Neural Influence Diffusion Model for Social Recommendation

By Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

566 citations recorded by the source corpus.

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, with these trusted users are influenced by the trusted users' social connections. 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 for top-10 recommendation on the two datasets.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9ae75ab09a9c0db08c5ec6a02ae940128f41e016

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. Yong Li
7. Chen Gao
8. Meng Wang

In Information Systems

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

Citations in the corpus

• Graph convolutional networks: a comprehensive review (2019, background): “propose a neural influence diffusion model that takes how users are influenced by their trusted friends into considerations for better social recommendations [124].”
• Diffusion Recommender Model (2023, background): “In addition, some studies on social recommendation consider information diffusion on social networks [43, 44].”
• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System (2020, background): “Personalized recommendation has revolutionized amyriad of online applications such as e-commerce [52, 55, 60], search engines [43], and conversational systems [29, 45].”
• Next-item Recommendation with Sequential Hypergraphs (2020, background): “In social recommendation, social connections between users can be investigated with GNN to model the propagation of user preference in social networks [10, 36, 40].”
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, methodology): “DiffNet [41] and its extension DiffNet++ [40] model the recursive dynamic social diffusion in social recommendation with a layer-wise propagation structure.”
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (2020, background): “For example, a recent trend is to exploit auxiliary information such as item knowledge graph [38], social network [41] and multimedia content [44] for recommendation, where GCNs have set up the new state-of-the-art.”
• Graph Neural Networks in Recommender Systems: A Survey (2020, methodology): “DiffNet [172] treats the influence of friends equally by leveraging mean-pooling operation.”
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022, background): “, user or item similarity) cannot be explicitly utilized for enriching the graph information, which has been shown essentially useful in recommendation tasks [24, 27, 35].”
• Graph Neural Networks for Recommender System (2022, background): “The scenario consists of social [24], sequential [4], session-”
• A Survey of Graph Neural Networks for Social Recommender Systems (2022, methodology): “Motivated by this, most recent works have focused on applying deep-learning techniques to So-cialRS, e.g., autoencoder [11, 119], generative adversarial networks ( GAN ) [35], and graph neural networks ( GNN ) [16, 105].”
• 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.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “ence among social neighbors, leading to the correlation of users’ interests in a social network [152], [153], [154], [155], [156], [157], [158].”
• Sequential Recommendation with Graph Neural Networks (2021, methodology): “GCN-based methods have achieved the state-of-the-art performance in other recommendation problems, such as social recommendation [6, 32, 40], knowledge graph-based recommendation [27, 28], multi-behavior recommendation [12], bundle recommendation [3], etc.”