Abstract Knowledge graph-based recommendation systems have strong capabilities in deep association mining and structured reasoning, which effectively alleviate data sparsity and cold-start problems in recommendation. However, traditional knowledge graph-based recommendation considers each graph relation in isolation, failing to capture the potential semantic correlations between different relation types, which leads to incomplete semantic representations. In addition, existing methods generally ignore negative feedback signals in users’ historical interactions, resulting in biased preference modeling. To overcome the above challenges, we introduce S ign-aware R ecommendation based on V irtual S emantic K nowledge G raph(SRVSKG), which enhances recommendation performance by combining virtual semantic collaborative representations with sign-aware learning techniques. Firstly, we propose a virtual semantic subgraph collaborative representation module to learn user and item embeddings. In this module, we build virtual semantic subgraphs through relation clustering based on latent semantic similarity measurement. Then a hierarchical feature extraction mechanism based on graph attention network is applied to virtual semantic subgraphs, which captures semantic associations across relations and enriches item embedding. At the same time, we emphasize user preference for attributes to enrich user embedding. Secondly, we design a sign-aware learning module, which constructs a user-item signed graph, applies Laplacian matrix factorization to simultaneously model the topological features of positive and negative feedback, and introduces the transformer architecture to dynamically fuse signed information, effectively utilizing negative feedback information to eliminate bias in user preference modeling. Finally, we establish a multi-feature fusion mechanism that deeply combines features from the virtual semantic subgraph collaborative representation module and the sign-aware learning module to enable recommendation. Experiment results on three public datasets demonstrate that SRVSKG outperforms state-of-the-art recommendation baselines.

Although PESQ (Perceptual Evaluation of Speech Quality) serves as an important benchmark for assessing narrow-band speech quality, its effectiveness depends on the availability of reference signals, which is not always feasible in practical applications. To surmount this limitation, numerous non-intrusive assessment methods have been proposed. However, most of them focus on wideband speech, with scant research dedicated to narrowband speech. To this end, this paper proposes a non-intrusive speech quality assessment model suitable for low-bitrate narrowband communication scenarios. The model integrates Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM) networks, and a frequency band attention mechanism, effectively extracting key features from narrowband speech signals and conducting quality evaluation. Experimental results show that the proposed model significantly outperforms the baselines, achieving a Mean Squared Error (RMSE) of 0.0200, a Pearson Linear Correlation Coefficient (PLCC) of 0.955, and a Spearman Rank Correlation Coefficient (SRCC) of 0.954, demonstrating higher accuracy and robustness.

Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then extract features by considering both of them. We also introduce a new dataset, named OpenLORIS-Location, to train the proposed network. The dataset contains 1553 images from 93 indoor locations. Various appearance changes between images of the same location are included and can help the model to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization. The RaPNet code and dataset are available at https://github.com/ivipsourcecode/RaP-Net.