Explainable recommendation based on knowledge graph and multi-objective optimization

Xie, Lijie; Hu, Zhaoming; Cai, Xingjuan; Zhang, Wensheng; Chen, Jinjun

doi:10.1007/s40747-021-00315-y

Cited by 54 publications

(29 citation statements)

References 42 publications

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“…Recent years have witnessed the growing attention on graph-based approaches [15,16,38,41]. Graph-based star identification algorithms have also emerged.…”

Section: Motivation and Related Workmentioning

confidence: 99%

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

You

Zhang

et al. 2022

Complex Intell. Syst.

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Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases.

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“…Recent years have witnessed the growing attention on graph-based approaches [15,16,38,41]. Graph-based star identification algorithms have also emerged.…”

Section: Motivation and Related Workmentioning

confidence: 99%

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

You

Zhang

et al. 2022

Complex Intell. Syst.

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“…The explainability algorithm relies on the KG to generate high-level, textual, and visual explanations. In contrast to the approaches in [15,24,25], where the IR and recommendation algorithms are model-intrinsic, the use of the KG as a SSoT in our framework allows the explainable IR algorithm to be model-agnostic. This removes the limits on the IR and explainability algorithms alike.…”

Section: Related Workmentioning

confidence: 99%

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

et al. 2022

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In modern industrial systems, collected textual data accumulates over time, offering an important source of information for enhancing present and future industrial practices. Although many AI-based solutions have been developed in the literature for a domain-specific information retrieval (IR) from this data, the explainability of these systems was rarely investigated in such domain-specific environments. In addition to considering the domain requirements within an explainable intelligent IR, transferring the explainable IR algorithm to other domains remains an open-ended challenge. This is due to the high costs, which are associated with intensive customization and required knowledge modelling, when developing new explainable solutions for each industrial domain. In this article, we present a transferable framework for generating domain-specific explanations for intelligent IR systems. The aim of our work is to provide a comprehensive approach for constructing explainable IR and recommendation algorithms, which are capable of adopting to domain requirements and are usable in multiple domains at the same time. Our method utilizes knowledge graphs (KG) for modeling the domain knowledge. The KG provides a solid foundation for developing intelligent IR solutions. Utilizing the same KG, we develop graph-based components for generating textual and visual explanations of the retrieved information, taking into account the domain requirements and supporting the transferability to other domain-specific environments, through the structured approach. The use of the KG resulted in minimum-to-zero adjustments when creating explanations for multiple intelligent IR algorithms in multiple domains. We test our method within two different use cases, a semiconductor manufacturing centered use case and a job-to-applicant matching one. Our quantitative results show a high capability of our approach to generate high-level explanations for the end users. In addition, the developed explanation components were highly adaptable to both industrial domains without sacrificing the overall accuracy of the intelligent IR algorithm. Furthermore, a qualitative user-study was conducted. We recorded a high level of acceptance from the users, who reported an enhanced overall experience with the explainable IR system.

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“…Similarly, Image super‐resolution can significantly improve the accuracy of multi‐target detection 32–34 . The framework based on knowledge graph and multi‐objective optimization 35 inspired me and provided new ideas for constructing super‐resolution models. Under the trend of IoT, the technology will show more tremendous advantages 36–39 …”

Section: Related Workmentioning

confidence: 99%

Multiscale generative adversarial network for real‐world super‐resolution

Sun

Yang

Tao

et al. 2021

Concurrency and Computation

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Summary Recently, most deep convolutional neural networks used for image super‐resolution have achieved impressive performance on ideal datasets. However, these methods always fail in real‐world super‐resolution, and the results are blurred and structurally deformed. In this paper, a multiscale generative adversarial network (MGAN) is proposed to alleviate these issues. The model's multiscale loss function can effectively reduce the solution space and obtain the best features to reconstruct the image. The degraded framework based on kernel estimation and noise injection is mainly applied to obtain LR images that share the same domain with real‐world pictures. Moreover, the gradient branch is presented to provide other structural priors for SR processing. Simultaneously, to obtain better visual effects, LPIPS is used for perceptual losses instead of Visual Geometry Group (VGG). The competitive results show that our MGAN model outperforms the state‐of‐the‐art methods, resulting in lower noise and better visual quality, and reflects the superiority in image structure restoration.

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Explainable recommendation based on knowledge graph and multi-objective optimization

Cited by 54 publications

References 42 publications

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

Multiscale generative adversarial network for real‐world super‐resolution

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