Knowledge-aware recommendation leverages rich item-related factual information in Knowledge Graphs (KGs) to enhance recommendation systems. However, most existing methods focus on developing complex models to extract information from a given KG. They essentially follow a model-centric paradigm, overlooking data quality problems. In practice, KG data exhibits two principal quality problems, namely the noisy knowledge problem and the incomplete knowledge problem, which severely impair the performance of downstream models. To address these problems, we adopt a data-centric paradigm to improve the quality of KG data. Inspired by diffusion models' superior denoising and generation ability by fitting true data distributions, we propose a novel spectral heterogeneous diffusion framework for knowledge-aware recommendation. This framework tailors a diffusion model to capture the recommendation-oriented heterogeneous distribution in the original KG and then converts the fitted distribution into a high-quality KG. Specifically, we design a spectral heterogeneous diffusion model that integrates recommendation prior knowledge to capture task-relevant distribution and aligns its diffusion process with the features of heterogeneous graphs to model heterogeneity. Furthermore, we propose a continuous-discrete mode adapter that transforms the learned continuous distribution into a high-quality discrete KG. The resulting KG is denoised and enriched with task-relevant triples, mitigating noisy and incomplete knowledge problems. Experiments show that our plug-and-play framework can be integrated with any knowledge-aware recommendation model and boost their performance by improving KG quality. The code and theoretical analyses are available at https://github.com/xiangmli/SHGD.

In the process of microseismic monitoring, the accuracy of seismic phase picking has a great influence on the precision of seismic localization. Recently, neural network models have achieved satisfying results in seismic phase picking. But these models are mainly applicable to natural earthquakes. In this letter, we present a fully convolutional network for microseismic phase picking called MSNet. The performance of the model is improved from three aspects: model structure, training data, and training strategy. MSNet has two decoders to allow for auxiliary learning, and its structure is improved with reference to state-of-the-art fully convolutional network. Our training data derives from the Stanford Earthquake Dataset (STEAD). It is selected and processed to meet our requirements. In the training process, a special early stopping strategy is used to obtain the model with best generalization performance. The model is tested on fracture-induced microseismic monitoring data from a coal mine in Shaanxi Province, China. Methods for comparison include short- and long-time average ratio, the original PhaseNet and a retrained PhaseNet. The number of correct P picks of MSNet is 18.7% more than PhaseNet, and MSNet achieves a 16.2% higher precision than PhaseNet on S phase. The event locating results verified the accuracy of MSNet. Our method provides an idea to build a microseismic phase picking model with better generalization performance and lower labor cost.