User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as one of the most successful approaches, but traditional UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously. Covering-based collaborative filtering (CBCF) is a useful approach that we have proposed in our previous work, which greatly improves the traditional UBCF and could provide satisfactory recommendations to an active user which often has sufficient rating information. However, different from an active user, a new user in RSs often has special characteristics (e.g., fewer ratings or ratings concentrating on popular items), and the previous CBCF approach cannot provide satisfactory recommendations for a new user. In this paper, aiming to provide personalized recommendations for a new user, through a detailed analysis of the characteristics of new users, we reconstruct a decision class to improve the previous CBCF and utilize the covering reduction algorithm in covering-based rough sets to remove redundant candidate neighbors for a new user. Furthermore, unlike the previous CBCF, our improved CBCF could provide personalized recommendations without needing special additional information. Experimental results suggest that for the sparse datasets that often occur in real RSs, the improved CBCF significantly outperforms those of existing work and can provide personalized recommendations for a new user with satisfactory accuracy and diversity simultaneously.

Long-tail service recommendation provides an unexpected but reasonable experience for potential developers when they construct mashups. However, the lack of available information makes it difficult to recommend highly relevant long-tail services for target mashups. Collaborative tagging systems employ extensive tag records to replenish the available information of long-tail services, whereas existing tag-aware approaches are unable to learn multi-aspect embeddings from graphs with different structures and relationships for long-tail services. To this end, we present a novel approach, namely collaborative tag-aware graph neural network, to recommend satisfactory long-tail services by extracting multi-aspect embeddings. Firstly, a tensor decomposition is executed to parameterize mashups, tags, and services as low-dimensional vector representations, respectively. Then, an interaction-aware heterogeneous neighbor aggregation is presented to aggregate both neighboring node features and interaction strength to enhance the embedding quality of long-tail services. Next, a diffusion-aware homogeneous neighbor aggregation is proposed to assign higher weights for long-tail neighboring nodes so as to reduce the influence of popular neighboring nodes during the aggregation process. Furthermore, a type-aware attention network is employed to update the final node embedding by aggregating multi-aspect embeddings. Experimental results on two real-world Web service datasets indicate that the proposed approach generates superior accuracy and diversity than state-of-the-art approaches in the aspect of long-tail service recommendation.

Human action recognition is the basis technology of human behavior understanding, and it is a research hotspot in the field of computer vision. Recently, some studies show skeleton data (i.e. joint points and edges) is naturally more conducive to mining the connotation of human action, so exploring the relationship between joints and bones is helpful to improve action recognition. In this work, regarding skeleton data as directed graph, we design a directed graph convolutional neural networks with a novel residual split structure. Firstly, we construct a directed graph represent model to extract human behavior by two kinds of graph models. Secondly, we use a novel residual split block to construct graph convolution neural network. Different from the traditional residual networks, we split high-dimensional features into several shallow features with the same dimension. It can not only ensure the diversity of mining features, but also avoid the gradient disappearing. Finally, during training, we use random sampling of data to reduce the burden of network training. Experiment results show that the proposed method achieves higher recognition rate than the comparative methods on the NTU-RGBD dataset.