Intelligent recommendation method integrating knowledge graph and Bayesian network

Pan, Hailan; Yang, Xiaohuan

doi:10.1007/s00500-021-05735-z

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Path-based methods consider the relational patterns linking users and items through KG entities, often referred to as meta-paths [22][23][24], to enhance the predictive capabilities of recommendation systems. In order to extract paths that convey valuable higher-order information, researchers have resorted to either deploying algorithms that pinpoint significant paths or crafting predefined meta-path templates to limit the scope of potential paths.…”

Section: Path-based Methodsmentioning

confidence: 99%

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

Peng,

Gong,

Zhou

et al. 2024

Electronics

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Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between user–item interaction data and knowledge graph information, resulting in insufficient model performance due to coarse-grained feature fusion. To bridge this gap, in this paper, we propose a novel graph neural network (GNN) model called KGCFRec, which leverages both Knowledge Graph and user–item Collaborative Filtering information for an enhanced Recommender system. KGCFRec employs a dual-channel information propagation and aggregation mechanism to generate distinct representations for the collaborative knowledge graph and the user–item interaction graph. This is followed by an attention mechanism that adaptively fuses the knowledge graph with collaborative information, thereby refining the representations and narrowing the gap between them. The experiments conducted on three real-world datasets demonstrate that KGCFRec outperforms state-of-the-art methods. These promising results underscore the capability of KGCFRec to enhance recommendation accuracy by integrating knowledge graph information.

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Section: Path-based Methodsmentioning

confidence: 99%

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

Peng,

Gong,

Zhou

et al. 2024

Electronics

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“…Since KG was formally introduced, it has grown as an essential component of AI in recent years [13].…”

Section: Process Kg-based Knowledge Reusementioning

confidence: 99%

A Knowledge-Guided Process Planning Approach with Reinforcement Learning

Zhang,

Wu,

Chen

et al. 2024

Preprint

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With the wide application of computer-aided technologies such as CAD and CAM in the manufacturing industry, more and more process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, more effective methods are needed to mine a large amount of multi-source information and the explicit and implicit relationships between knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning problem as a sequential recommendation of process knowledge. Secondly, this paper designs in detail the state representation that combines process sequences and potential relationships between processes. This paper also creates a composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure proposed in this paper has more accurate recommendation results than other methods. Finally, this paper takes flange as an example to verify the feasibility and effectiveness of the proposed method.

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“…At present, many researchers have devoted themselves to the research of multi-classifier system for dance emotion recognition. Bhosle and Deshmukh (2019) and Pan and Yang (2021), the fusion of KNN, radial basis function (RBF), and Bayesian network was proposed, and the accuracy reached 71.40%. Jacob and Mythili (2015) proposed to connect NBC in layers to extract prosodic features such as pitch, energy, duration, and zero crossing rate.…”

Section: Emotion Feature Dimension Reductionmentioning

confidence: 99%

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Shen

Xiao-xi²,

Jiang³

et al. 2022

Front. Neurorobot.

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Dance emotion recognition is an important research direction of automatic speech recognition, especially in the robot environment. It is an important research content of dance emotion recognition to extract the features that best represent speech emotion and to construct an acoustic model with strong robustness and generalization. The dance emotion data set is small in size and high in dimension. The traditional recurrent neural network (RNN) has the problem of long-range dependence disappearance, and due to the focus on local information of convolutional neural network (CNN), the mining of potential relationships between frames in the input sequence is insufficient. To solve the above problems, this paper proposes a novel linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory (LSTM) for dance emotion recognition. In this paper, the linear prediction coefficient (LPC) and Meier frequency cepstrum coefficient (MFCC) are combined to obtain a new feature, namely the linear prediction Meier frequency cepstrum coefficient (LPMFCC). Then, the combined feature obtained by combining LPMFCC with energy feature is used as the extracted dance feature. The extracted features are input into the bidirectional LSTM network for training. Finally, support vector machine (SVM) is used to classify the obtained features through the full connection layer. Finally, we conduct experiments on public data sets and obtain the better effectiveness compared with the state-of-art dance motion recognition methods.

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Intelligent recommendation method integrating knowledge graph and Bayesian network

Cited by 11 publications

References 19 publications

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

A Knowledge-Guided Process Planning Approach with Reinforcement Learning

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

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