Collaborative filtering via heterogeneous neural networks
After being proved extremely useful in many applications, the network embedding has played a critical role in the network analysis. Most of recent works usually model the network by minimizing the joint probability that the target node co-occurs with its neighboring nodes. These methods may fail to capture the personalized informativeness of each vertex. In this work, we propose a method named Personalized Proximity Preserved Network Embedding (PPPNE) to adaptively capture the personalization of vertices based on the personalized ranking loss. Our theoretical analysis shows that PPPNE generalizes prior work based on the matrix factorization or the neural network with a single layer, and we argue that preserving personalized proximity is the key to learning more informative representations. Moreover, to better capture the network structure in multiple scales, we exploit the distance ordering of each vertex. Our method can be efficiently optimized with a vertex-anchored sampling strategy. The results of extensive experiments on five real-world networks demonstrate that our approach outperforms state-of-the-art network embedding methods with a considerable improvement on several common tasks including link prediction and vertex classification. Additionally, PPPNE is efficient and can be easily accelerated by parallel computing, which enables PPPNE to work on large-scale networks.
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