Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

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

doi:10.1109/ijcnn48605.2020.9207610

Cited by 11 publications

(8 citation statements)

References 21 publications

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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

Self Cite

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“…Several recent works which introduced GNN to TRS have demonstrated its effectiveness to deepen the use of subgraph with high-hop neighbors, such as TGCN [4], GNN-PTR [12] and TA-GNN [10]. However, those works have shown promising results; we argue that its designs are rather burdensome, directly inherited from GCN without justification.…”

Section: Introductionmentioning

confidence: 86%

LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation

Zhang¹,

Xu²,

Wu³

et al. 2022

Preprint

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Tag-aware recommendation is a task of predicting a personalized list of items for a user by their tagging behaviors. It is crucial for many applications with tagging capabilities like last.fm or movielens. Recently, many efforts have been devoted to improving Tag-aware recommendation systems (TRS) with Graph Convolutional Networks (GCN), which has become new state-of-the-art for the general recommendation. However, some solutions are directly inherited from GCN without justifications, which is difficult to alleviate the sparsity, ambiguity, and redundancy issues introduced by tags, thus adding to difficulties of training and degrading recommendation performance.In this work, we aim to simplify the design of GCN to make it more concise for TRS. We propose a novel tag-aware recommendation model named Light Folksonomy Graph Collaborative Filtering (LFGCF), which only includes the essential GCN components. Specifically, LFGCF first constructs Folksonomy Graphs from the records of user assigning tags and item getting tagged. Then we leverage the simple design of aggregation to learn the high-order representations on Folksonomy Graphs and use the weighted sum of the embeddings learned at several layers for information updating. We share tags embeddings to bridge the information gap between users and items. Besides, a regularization function named TransRT is proposed to better depict user preferences and item features. Extensive hyperparameters experiments and ablation studies on three real-world datasets show that LFGCF uses fewer parameters and significantly outperforms most baselines for the tag-aware top-N recommendations.

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“…It only uses the item-tag matrix information • PITF [8] It explicitly models the pairwise interaction between users, items, and tags, which is a strong competitor in the field of personalized tag recommendation. • GNN-PTR [17] It is a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. • Bi-GRU+Att [11] This is a content-based tag recommendation method, where deep learning methods are utilized for capturing semantic meanings in the text.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…Fang et al [16] exploited the Gaussian radial basis function to increase the model's capacity which could be considered as a nonlinear extension of Canonical Decomposition. Chen et al [17] integrated the graph neural networks into the pairwise interaction tensor factorization model to better capture the tagging patterns in item-tag interaction graph.…”

Section: Tag Recommendationmentioning

confidence: 99%

Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation

Yi¹,

Ren²,

Chen³

2022

Preprint

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Recommending appropriate tags to items can facilitate content organization, retrieval, consumption and other applications, where hybrid tag recommender systems have been utilized to integrate collaborative information and content information for better recommendations. In this paper, we propose a multi-auxiliary augmented collaborative variational auto-encoder (MA-CVAE) for tag recommendation, which couples item collaborative information and item multi-auxiliary information, i.e., content and social graph, by defining a generative process. Specifically, the model learns deep latent embeddings from different item auxiliary information using variational auto-encoders (VAE), which could form a generative distribution over each auxiliary information by introducing a latent variable parameterized by deep neural network. Moreover, to recommend tags for new items, item multi-auxiliary latent embeddings are utilized as a surrogate through the item decoder for predicting recommendation probabilities of each tag, where reconstruction losses are added in the training phase to constrict the generation for feedback predictions via different auxiliary embeddings. In addition, an inductive variational graph auto-encoder is designed where new item nodes could be inferred in the test phase, such that item social embeddings could be exploited for new items. Extensive experiments on MovieLens and citeulike datasets demonstrate the effectiveness of our method.

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Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Cited by 11 publications

References 21 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

LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation

Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation

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