Enhanced Multi-Task Learning and Knowledge Graph-Based Recommender System

Gao, Min; Li, Jianyu; Chen, Chunhua; Li, Yun; Zhang, Jun; Zhan, Zhi‐Hui

doi:10.1109/tkde.2023.3251897

Cited by 44 publications

(15 citation statements)

References 51 publications

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“…In light of the connection between graph completion and related tasks, some scholars have explored the application of multi-task learning (MTL) strategies to mitigate the impact of disparities between KGE and recommendation tasks, and to enhance the overall performance of both tasks. MTL involves the collective acquisition of knowledge and simultaneous or sequential learning of multiple tasks, which is deemed more advantageous than training them in isolation [18]. In the context of MTL-based recommendation systems, the establishment of links between multiple tasks and the training of these tasks are recognized as pivotal concerns.…”

Section: Knowledge Graph Embeddingmentioning

confidence: 99%

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

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Section: Knowledge Graph Embeddingmentioning

confidence: 99%

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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“…Furthermore, knowledge graphs excel in task analysis mainly due to their semantic relationship models, rich knowledge representations, semantic reasoning capabilities, structured representations, and support for large-scale data. These features enable knowledge graphs to better understand tasks and provide accurate, comprehensive information support for them [27]. Users Zhong et al Journal of Cloud Computing (2024) 13:114 can query the knowledge graph to discover hidden relations between different entities or explore the knowledge structure within a specific domain.…”

Section: Basic Concepts Of Knowledge Graphsmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

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In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

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“…Multi-task learning (MTL) in recommender systems, which involves learning related tasks jointly for performance improvement, is also receiving increased attention [39,40]. Various types of tasks are being explored in this context, including but not limited to rating prediction and auxiliary tasks.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature Integration

Wang,

Jin,

Zhang

et al. 2024

Information

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Personalized recommender systems play a crucial role in assisting users in discovering items of interest from vast amounts of information across various domains. However, developing accurate personalized recommender systems remains challenging due to the need to balance model architectures, input feature combinations, and fusion of heterogeneous data sources. This study investigates the impacts of these factors on recommendation performance using the MovieLens and Book Recommendation datasets. Six models, including single-task neural networks, multi-task learning, and baselines, were evaluated with various input feature combinations using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The multi-task learning approach achieved significantly lower RMSE and MAE by effectively leveraging heterogeneous data sources for personalized recommendations through a shared neural network architecture. Furthermore, incorporating user data and content data progressively enhanced performance compared to using only item identifiers. The findings highlight the importance of advanced model architectures and fusing heterogeneous data sources for high-quality recommendations, providing valuable insights for designing effective recommender systems across diverse domains.

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Enhanced Multi-Task Learning and Knowledge Graph-Based Recommender System

Cited by 44 publications

References 51 publications

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature Integration

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