A Neural Influence Diffusion Model for Social Recommendation (1904.10322v1)

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.

A Neural Influence Diffusion Model for Social Recommendation

The paper "A Neural Influence Diffusion Model for Social Recommendation" presents a novel approach to enhance social recommendation systems by simulating the recursive social influence diffusion process in social networks. The authors, Le Wu et al., propose DiffNet, a deep influence propagation model designed to address limitations in existing social recommender systems that typically do not incorporate the dynamic and recursive nature of social influences.

Research Context

The research is motivated by the need to improve user and item embedding learning, which is a critical factor in the performance of recommender systems. Traditional collaborative filtering (CF) techniques suffer from data sparsity issues, limiting their effectiveness. With the rise of online social networks, leveraging social connections to enhance the recommendation process has become a plausible solution. Existing social recommendation models generally rely on static user connections without considering the iterative nature of influence diffusion.

Methodology

The core innovation of the DiffNet model is its structured approach to simulate how social influence propagates through the network over time. This is achieved through a layer-wise influence propagation structure. First, each user is assigned an initial embedding that integrates user features and latent behavior preferences. As influence diffuses through the network, these embeddings are recursively updated, capturing both direct and indirect social influences. This recursive process reflects a more realistic method of simulating user interest changes over time due to social interactions.

DiffNet's layer-wise architecture allows the model to be flexible and general; it can function effectively even if user/item attributes or the explicit social network structure are not available. The model's final embeddings can also be incorporated seamlessly into existing CF models such as BPR and SVD++, facilitating its integration into a variety of recommendation frameworks.

Results

Empirical validation of DiffNet is conducted using two real-world datasets, Yelp and Flickr. The proposed model demonstrates a more than 13% performance improvement over existing baselines in top-10 recommendations, underscoring its effectiveness. The results highlight DiffNet's ability to leverage social networks to mitigate data sparsity and enhance recommendation accuracy by modeling influence diffusion iteratively.

Implications and Future Directions

The work presented in this paper holds significant implications for both practical applications and theoretical advancements in recommender systems. Practically, DiffNet offers a robust solution for platforms leveraging social data to improve user experience via tailored recommendations. Theoretically, it extends the understanding of social network-based recommendation by introducing a dynamic influence model rather than a static one, opening avenues for further research in incorporating temporal aspects of social influence and integrating broader types of social data.

Future research directions could explore enhancements in DiffNet's deep learning architecture, allowing it to scale to even larger networks while maintaining efficiency. Additionally, extending the model to incorporate temporal dynamics explicitly, thereby addressing the time-varying nature of user preferences, would be a promising step. Given the model's flexibility, applying it to domains beyond e-commerce, such as content streaming services or social content recommendation, could also be beneficial.

In summary, the DiffNet model provides a comprehensive framework for advancing social recommendation systems by capitalizing on the recursive nature of social influence, thus offering superior performance in sparse data environments and enriching the theoretical landscape of recommendation methodologies.