Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
This addresses data sparsity and cold-start issues in recommender systems for users and items, but it is incremental as it builds on existing social recommendation models.
The paper tackled the problem of static social effects in social recommendation by proposing dual graph attention networks to model dynamic and context-aware social effects, achieving great improvement in recommendation accuracy compared to state-of-the-art methods.
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.