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.

该论文提出了一种基于深度影响传播模型的社交推荐方法，模拟了用户在全局社交网中的递归扩散过程，取得了比最佳基线模型更优异的性能提升。