Jiwei Hu

The fusion modeling of intra-session item information representation and inter-session item transition pattern for session recommendation has shown performance advantages. However, existing research still suffers from the following challenges: (1) the time-varying effects of complex relationships between item transitions within sessions need to be deeply explored; and (2) the lack of effective representation for inter-session item transition patterns. To address these challenges, we propose a new session recommendation, named EGDLTP-CCL. Specifically, we first design a patch-enhanced gated neural network representation of session item transition patterns, which accurately captures the time-dynamically varying impacts of the complex relationships within sessions of item transitions through a designed transformer patching strategy. Then, we develop an energy-constraint-based graph diffusion model to capture the inter-session item transition patterns, which mitigates the problem of poor simulation of real inter-session item transition patterns by the introduction of an energy-constraint strategy for the graph diffusion model. In addition, patch-enhanced gated neural networks and energy-constrained graph diffusion models are treated as two different views in the contrastive learning framework. By introducing a curriculum learning strategy that explores how to effectively select and train negative samples in a contrastive learning framework, thereby deeply improving performance in contrastive learning task. Finally, we combine and jointly train the recommendation task and the curriculum learning contrastive learning task for optimization based on a multi-task learning strategy to further improve the recommendation performance. Experiments on real-world datasets show that EGDLTP-CCL significantly outperforms state-of-the-art methods.

In recent years, it has become a hot topic to combine graph neural networks with contrastive learning, a method which has been applied not only in recommendation tasks but has also achieved impressive results in many fields, such as text processing and spatio-temporal modeling. However, existing methods are still constrained by several issues: (1) Most graph learning methods do not explore the imbalance of node and edge type distribution caused by different user–item interactions. (2) The randomness of data expansion and sampling strategies in contrastive learning may lead to confusion about the importance of key items for users. To overcome the problems, in this paper, we propose an explanation-guided contrastive recommendation model based on interactive message propagation and dual-hypergraph convolution (ECR-ID). Specifically, we designed two different interactive propagation mechanisms for the user–item dual-hypergraph sets to promote comprehensive dynamic information propagation and exchange, which further mitigates the imbalance problem of hyperedges and nodes in the hypergraph convolution, as well as the propagation loss of synergistic information between nodes. In addition, we developed an explanation-guided contrastive learning framework, which highlights the important items in user–item interactions through an explanation-based approach and guided the training of the contrastive learning framework based on the differences in the importance scores of the items, thus generating accurate positive and negative views and improving the contrastive learning performance. Finally, we integrated the contrastive learning framework with the dual-hypergraph networks based on joint training to further improve the recommendation performance. Extensive experimental evaluations on real datasets show that ECR-ID outperforms state-of-the-art recommendation algorithms. In the future, we will conduct in-depth tests based on a wider range of real-world datasets to alleviate the limitation that the existing experimental datasets all comprise data from single business services like Alibaba and Amazon, thus validating the effectiveness of our model more comprehensively.

This paper proposes a derivative-variance hybrid global sensitivity measure with optimal sampling method selection. The proposed sensitivity measure is as computationally efficient as the derivative-based global sensitivity measure, which also serves as the conservative estimation of the corresponding variance-based global sensitivity measure. Moreover, the optimal sampling method for the proposed sensitivity measure is studied. In search of the optimal sampling method, we investigated the performances of six widely used sampling methods, namely Monte Carlo sampling, Latin hypercube sampling, stratified sampling, Latinized stratified sampling, and quasi-Monte Carlo sampling using the Sobol and Halton sequences. In addition, the proposed sensitivity measure is validated through its application to a rural bridge.

5G technology puts forward new requirements for communication network human maintenance services. However, with the complexity of 5G maintenance knowledge and the diversification of maintenance scenarios, the ability of maintenance engineer cannot be greatly improved in a short time. This paper proposed a knowledge graph (KG) based augmented reality (AR) content adaptive generation method for 5G communication network maintenance services. The KG and AR technology are used to reduce the cognitive burden of manual maintenance and improve the efficiency of communication network maintenance.