Neural Graph for Personalized Tag Recommendation

Yu, Yonghong; Chen, Xuewen; Zhang, Li; Gao, Rong; Gao, Haiyan

doi:10.1109/mis.2020.3040046

Cited by 13 publications

(6 citation statements)

References 19 publications

(30 reference statements)

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“…However, GraphRec adopts a classic GNN framework, i.e., a heavyweight GNN, to aggregate neighborhood information, while our proposed recommendation model aggregates neighbors’ representations by utilizing a lightweight GNN framework that abandons the feature transformation and nonlinear activation components. This finding is consistent with several recommendation models based on lightweight GNNs [ 9 , 29 , 30 ], which show that the feature transformation and nonlinear activation components barely contribute to the recommendation quality.…”

Section: Empirical Analysissupporting

confidence: 91%

A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Qian

Zhang

et al. 2022

Sensors

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Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users’ and items’ latent representations, resulting in suboptimal recommendation performance. In this paper, we propose a graph neural network (GNN)-based social recommendation model that utilizes the GNN framework to capture high-order collaborative signals in the process of learning the latent representations of users and items. Specifically, we formulate the representations of entities, i.e., users and items, by stacking multiple embedding propagation layers to recursively aggregate multi-hop neighborhood information on both the user–item interaction graph and the social network graph. Hence, the collaborative signals hidden in both the user–item interaction graph and the social network graph are explicitly injected into the final representations of entities. Moreover, we ease the training process of the proposed GNN-based social recommendation model and alleviate overfitting by adopting a lightweight GNN framework that only retains the neighborhood aggregation component and abandons the feature transformation and nonlinear activation components. The experimental results on two real-world datasets show that our proposed GNN-based social recommendation method outperforms the state-of-the-art recommendation algorithms.

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Section: Empirical Analysissupporting

confidence: 91%

A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Qian

Zhang

et al. 2022

Sensors

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“…Furthermore, the aforementioned A-BiLSTM architecture implemented in this research was shown to be highly effective, but with further experimentation with different layer and hyperparameter settings [24][25][26][27][28][29][30][31][32], additional improvements in performance could be made. Evolutionary algorithms [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] could also be exploited pertaining to the above parameter tuning as well as architecture generation processes. Moreover, it would also be beneficial to employ additional medical audio datasets to further evaluate model efficiency.…”

Section: Discussionmentioning

confidence: 99%

Deep Recurrent Neural Networks with Attention Mechanisms for Respiratory Anomaly Classification

Wall

Zhang

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses.

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“…Lu et al developed a post-based collaborative filtering method [32] based on a ternary social tag network. To capture higher-order collaborative signals in entity interactions, Yu et al applied graph networks to PITF to aggregate neighbor information from multiple layers to generate the final representation of entity pairs [33]. Wang et al extended PITF by adding weights to user-tag interactions and item-tag interactions separately considering both temporal factors and personalization [34].…”

Section: Related Work a Tag Recommendationmentioning

confidence: 99%

Pairwise Metric Learning With Angular Margin for Tag Recommendation

et al. 2023

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The users on the Internet have been growing exponentially, and tag recommendation can automatically provide users with a selection of tags of interest to meet their personalized needs. Users can utilize these tags to freely annotate their favorite resources, making them efficient and fast in retrieving related resources. Tensor factorization methods are commonly used in tag recommendation at present. These methods model the user × item × tag interactions, transform the latent feature representations of users, items and tags into low-rank matrices and use inner products for prediction. However, the problem of using inner product is that it does not satisfy the triangle inequality, it ignores the distance relationship among entity pairs and cannot capture the fine-grained preference information. Metric learning in recommendation domains focus on using pairwise loss, which assumes that different categories (such as users, items and tags) have fixed margins. Different categories often have different intra-class variations. With fixed margins, it is often difficult to accurately distinguish between positive and negative samples, thus reducing recommendation performance and limiting the expression ability of the model. In this study, the metric learning method is used to explore the distance relationship among user × item × tag triplet, and the existing metric learning based methods (namely LRML, CML, SML) are applied to the tag recommendation. A pairwise metric learning method with angular margin is proposed, named PMLT. The pairwise distance relationship between user-tag and item-tag is modeled for the information of different entities. And an extra angular margin regularizer is added to the original pairwise loss to control the size of angular margin for user-tag and item-tag respectively. The strength of the constrained angular margin regularizer is controlled to dynamically adjust the distance changes of entity. This method constrains the fixed margin and also the angular margin of user-tag and item-tag. Compared with the traditional metric learning method, this method can capture additional relationship structure and has good recommendation performance. Finally, we conducted extensive experiments on two datasets, LastFm and Movielens, and the experimental results showed that the proposed method PMLT outperform the state-of-the-art baseline in the evaluation metrics Recall@N and NDCG@N, and obtain better prediction quality. We also analyze the influence of different parameters and internal components on the performance of the proposed method, which improves the interpretability of the proposed method.INDEX TERMS Tag recommendation, tensor factorization, metric learning, angular margin.

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Neural Graph for Personalized Tag Recommendation

Cited by 13 publications

References 19 publications

A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Deep Recurrent Neural Networks with Attention Mechanisms for Respiratory Anomaly Classification

Pairwise Metric Learning With Angular Margin for Tag Recommendation

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