Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems (1903.10433v1)

Abstract: Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of constant weights or fixed constraints. To relax this strong assumption, in this paper, we propose dual graph attention networks to collaboratively learn representations for two-fold social effects, where one is modeled by a user-specific attention weight and the other is modeled by a dynamic and context-aware attention weight. We also extend the social effects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that different social effects in two domains could interact with each other and jointly influence user preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social effects. Experiments on one benchmark dataset and a commercial dataset verify the efficacy of the key components in our model. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.

An Overview of Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

The paper "Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems" presents an advanced architecture named DANSER, which leverages dual graph attention networks to enhance the effectiveness of social recommendation systems. This approach addresses key challenges inherent in traditional collaborative filtering methods, namely, data sparsity and cold-start problems, by incorporating and modeling multifaceted social effects.

Overview of DANSER Architecture

1. Dual Graph Attention Networks (GATs): The architecture leverages dual GATs to model social effects both in the user domain and the item domain. This duality encompasses two separate but complementary social effects: social homophily, reflecting long-term similarity in user preferences, and social influence, highlighting dynamic and context-specific impacts of other users. In the item domain, analogous effects relate to item-to-item homophily and influence, which capture static and dynamic attributes, respectively.
2. Modeling Social Effects:
   • User Domain: A GAT is deployed to capture user-specific static and dynamic preferences by examining friend influence in a flexible and context-aware manner. Static preferences derive from inherent user similarities, while dynamic preferences adjust for specific contexts using attention mechanisms.
   • Item Domain: The paper extends the notion of social effects to items, proposing a GAT for both the static similarity (item-to-item homophily) and dynamic relevance (item-to-item influence) between items.
3. Policy-based Fusion Strategy: The paper introduces a novel strategy that employs a multi-armed bandit framework to dynamically weigh the interactions of the social effects mentioned above based on specific user-item pair contexts. This policy-based approach allows the model to adaptively capture the prominence of each social effect, thus enhancing recommendation accuracy.

Numerical Results and Implications

The experimental results are compelling, demonstrating that DANSER outperforms state-of-the-art social recommendation methods across various datasets. Notably, it achieves a 2.9% improvement in mean absolute error (MAE) on Epinions and a 4.5% improvement in area under the curve (AUC) on WeChat Top Story compared with prior models. These enhancements underscore the efficacy of incorporating both user-specific and item-specific social effects in a dynamic and context-sensitive fashion.

Theoretical and Practical Implications

The approach forwarded by DANSER has significant implications for the design and implementation of recommender systems. From a theoretical standpoint, it further elucidates the nuanced interplay of static and dynamic social effects, challenging the assumption that social relationships in social networking services reflect uniform preferences. Practically, the model offers a path forward for enhanced personalization in recommendation systems, driven by its ability to adaptively balance various social influences, thus holding promise for a wide array of applications from content recommendation to e-commerce.

Future Directions in AI

The methodologies proposed in this paper spark interest in exploring similar dual-layered architectures for other domains that can benefit from dynamic modeling of complex relational data. Further research could explore the integration of additional auxiliary data sources or develop more sophisticated policy mechanisms for even richer context adaptation. Additionally, scalability to larger and more diverse datasets, as well as real-time adaptability, remain areas for future improvement and exploration.

In conclusion, the paper's methodological advancements contribute significantly to the ongoing development of socially-aware recommendation systems, offering deeper insights into how social dynamics can be effectively harnessed to address longstanding issues in recommender systems.