GCMCSR: A New Graph Convolution Matrix Complete Method with Side-Information Reconstruction

Niu, Kun; Yu, Yicong; Cao, Xipeng; Wang, Chao

doi:10.1109/icdmw51313.2020.00033

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…e results in Figure 8 show that the recommended proposed model achieves better experimental results than other comparative methods ( [19][20][21][22][23]) on the dataset. Compared with the top performance of literature [20], the NDCG of the proposed model is improved by 5.6%.…”

Section: Results Of Datasetmentioning

confidence: 97%

“…Five other models were selected as comparison methods for the experiments, including graph-based collaborative filtering methods [19], graph convolutional neural networkbased recommendation algorithms [20], matrix decomposition model-based recommendation algorithms [21], GCMCSR [22], and Wide&Deep [23]. Figure 8 illustrates the performance results of Recall and NDCG for top-5.…”

Section: Results Of Datasetmentioning

confidence: 99%

“…Compared with the top performance of literature [20], the NDCG of the proposed model is improved by 5.6%. From the analysis of the experimental results, literature [22] uses GNN based on the information transfer paradigm to mine the dichotomous graphs and model the collaborative filtering signals directly into the node representations. e performance is improved compared to the traditional neural network model.…”

Section: Results Of Datasetmentioning

confidence: 99%

“…e performance is improved compared to the traditional neural network model. Literature [19] achieves better performance compared to literature [22] by explicitly modeling the higher-order connectivity between users and items to improve the quality of node representations. Literature [20] only performs normalized neighborhood embedding for the next layer.…”

Section: Results Of Datasetmentioning

confidence: 99%

“…e role of the hyperparameter β 1 is to control the proportion of the contrast learning assistance task to the joint learning task. e optimum performance is reached when β 1 � 0.1, according to the experimental results in Literature [20] Literature [21] Literature [22] Literature [23] Proposed Recall and NDCG@5 Models Recall@5 NDCG@5 e experimental results are compared under different values of the temperature hyperparameter to verify the effect of the temperature hyperparameter on the algorithm's performance. Figure 10 depicts the experimental outcomes.…”

Section: Analysis Of Hyperparametric Resultsmentioning

confidence: 99%

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A Study on the Construction of Translation Curriculum System for English Majors from the Perspective of Human-Computer Interaction

2022

Advances in Multimedia

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This paper proposes a collaborative human-computer interaction recommendation model for English major translation courses to address the problems of poor course recommendation and lack of robustness to noisy data in traditional recommendation models for English major translation courses. First, a new data enhancement method is proposed for the bipartite graph structure. Then, the enhanced data is fed into a graph convolutional neural network for node feature extraction to obtain node representations of users and items. A recommendation supervision task and an auxiliary task for contrast learning are constructed for joint optimization. The human-computer interaction model of the knowledge graph is designed, and the dialogue entities are embedded in the knowledge graph ripple network to obtain potentially interesting content for students. Finally, the student interaction content and node representations are combined to obtain the optimal translation course recommendation. The experimental results indicate that the proposed model in this work is capable of producing higher-quality English major course recommendations and beats other current models. This model is suitable for English primary translation course content recommendation and helps to improve students’ translation ability.

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