A Hybrid Recommendation System Based on the Supply Chain in Social Networks

The case assignment system is an essential system of case management and assignment within the procuratorate and is an important aspect of judicial fairness and efficiency. However, existing methods mostly use manual or random case assignment, which leads to unbalanced case distribution. Moreover, the relationship between prosecutors and case categories usually shows a power-law distribution in real-world data. Therefore, in this paper, we describe the case rationality assignment as a recommendation problem under the power-law distributed data. To solve the above problems, we propose an end-to-end Self-supervised Graph neural network model with Pre-training Generative learning for Recommendation (SGPGRec), the main idea of which is to capture self-supervised signals using intra-node features and inter-node correlations in the data, and generate the data representation by pre-training to improve the recommendation results. To be specific, we designed three auxiliary self-supervised tasks based on the prosecutor-case category interaction graph and the data distribution to obtain feature representations of prosecutors, case categories, and the interaction information between them. Then we constructed an end-to-end graph neural network recommendation model by the interaction information based on the data characteristics of the power-law distribution. Finally, extensive experimental consistency on a real-world dataset from three procuratorates shows that our method is effective compared to several yet competing baseline methods and further supports the development of an intelligent case assignment system with adequate performance.

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Self-supervised graph neural network with pre-training generative learning for recommendation systems

Min

Yang

et al. 2022

Sci Rep

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Multi-stakeholder recommendations system with deep learning-based diversity personalization and multi-objective optimization for establishing trade-off among competing preferences

Shrivastava,

Sisodia,

Nagwani

2024

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PurposeThe Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.Design/methodology/approachThe proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.FindingsThe experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.Originality/valueThe proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

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A Prospective Metaverse Paradigm Based on the Reality-Virtuality Continuum and Digital Twins

Zare,

Jalali

2025

RACSC

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After decades of introducing the concept of virtual reality, the expansion, and significant advances of technologies and innovations, such as 6g, edge computing, the internet of things, robotics, artificial intelligence, blockchain, quantum computing, and digital twins, the world is on the cusp of a new revolution. By moving through the three stages of the digital twin, digital native, and finally surrealist, the metaverse has created a new vision of the future of human and societal life so that we are likely to face the next generation of societies (perhaps society 6) in the not too distant future. However, until then, the reality has been that the metaverse is still in its infancy, perhaps where the internet was in 1990. There is still no single definition, few studies have been conducted, there is no comprehensive and complete paradigm or clear framework, and due to the high financial volume of technology giants, most of these studies have focused on profitable areas such as gaming and entertainment. The motivation and purpose of this article are to introduce a prospective metaverse paradigm based on the revised reality-virtuality continuum and provide a new supporting taxonomy with the three dimensions of interaction, immersion, and extent of world knowledge to develop and strengthen the theoretical foundations of the metaverse and help researchers. Furthermore, there is still no comprehensive and agreed-upon conceptual framework for the metaverse. To this end, by reviewing the research literature, discovering the important components of technological building blocks, especially digital twins, and presenting a new concept called meta-twins, a prospective conceptual framework based on the revised reality-virtuality continuum with a new supporting taxonomy was presented.

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A Hybrid Recommendation System Based on the Supply Chain in Social Networks

Cited by 4 publications

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Self-supervised graph neural network with pre-training generative learning for recommendation systems

Self-supervised graph neural network with pre-training generative learning for recommendation systems

Multi-stakeholder recommendations system with deep learning-based diversity personalization and multi-objective optimization for establishing trade-off among competing preferences

A Prospective Metaverse Paradigm Based on the Reality-Virtuality Continuum and Digital Twins

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