Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks

Dai, Quanyu; Wu, Xiao-Ming; Lü, Fan; Li, Qimai; Liu, Han; Zhang, Xiaolin; Wang, Dan; Lin, Guli; Yang, Keping

doi:10.1016/j.patcog.2022.108628

Cited by 30 publications

(5 citation statements)

References 7 publications

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“…Inspired by the latest progress in node/graph classification methods [11], this paper is based on the method of the spectral graph theory and uses the multivariate implicit information in the field of graphs to overcome the abovementioned shortcomings and challenges. Specifically, in order to overcome the difficulty of learning recommendation directly from the spectral domain, this paper proposes a new spectral convolution operation, which is approximated by the Chebyshev first-order truncation and dynamically amplifies or attenuates each frequency domain [12]. Then, this paper introduces a recommendation model: multivariate fusion spectral convolution collaborative filtering (CBSVD-SCF), as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Wang

Zeng

Song

et al. 2022

Wireless Communications and Mobile Computing

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To solve the abovementioned problem, we propose a collaborative filtering recommendation algorithm that incorporates singular value decomposition (SVD) and Chebyshev truncation in spectral domain convolution. Firstly, the SVD algorithm is used to optimize the adjacency matrix, mine the potential association information between users and items, and expand the user-item adjacency matrix. Finally, based on the MovieLens-1M public dataset, the proposed algorithm (CBSVD-SCF) is compared with other commonly used algorithms. The results show that the article optimizes the recommendation effect of the algorithm based on the traditional collaborative filtering algorithm by combining the temporal order and sequence of user interaction information, as well as the popularity of items and the activity of users; the experimental results on MovieLens show that the optimized collaborative filtering recommendation algorithm can effectively improve the recommendation effect.

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Section: Introductionmentioning

confidence: 99%

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Wang

Zeng

Song

et al. 2022

Wireless Communications and Mobile Computing

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“…The proposed framework enables direct interpretation of the reasoning behind each recommendation by visualizing the factors that contributed to it. [Dai et al, 2022] proposed a novel framework with collaborative and attentive graph convolutional networks for personalized knowledge-aware recommendation. Particularly, we model the user-item graph and the KG separately and simultaneously with an efficient graph convolutional network and a personalized knowledge graph attention network, where the former aims to extract informative collaborative signals, while the latter is designed to capture fine-grained semantics.…”

Section: Related Workmentioning

confidence: 99%

Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations

Boughareb,

Seridi-Bouchelaghem,

Beldjoudi

2023

JWE

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The latest advances in Graph Neural Networks (GNNs), have provided important new ideas for solving the Knowledge Graph (KG) representation problem for recommendation purposes. Although GNNs have an effective graph representation capability, the nonlinear transformations over the layers cause a loss of semantic information and make the generated embeddings hard to explain. In this paper, we investigate the potential of large KGs to perform interpretable recommendation using Graph Attention Networks (GATs). Our goal is to fully exploit the semantic information and preserve inherent knowledge ported in relations by jointly learning low-dimensional embeddings for nodes (i.e., entities) and edges (i.e., properties). Specifically, we feed the original data with additional knowledge from the Linked Open Data (LOD) cloud, and apply GATs to generate a vector representation for each node on the graph. Experiments conducted on three real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. In addition to improving predictive performance in terms of precision, recall, and diversity, our approach fully exploits the rich structured information provided by KGs to offer explanation for recommendations.

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“…The knowledge graph (KG), as an important part of the field of artificial intelligence, has gradually become a research hotspot, which enables machines to better understand and use knowledge through structured data. The applications supported by the knowledge graph are increasingly diverse and have been successfully applied to tasks such as intelligent question answering [1][2][3], personalized recommendations [4][5][6], and interpretable tools [7][8][9]. With the increase in the application of the knowledge graph, people have built large-scale non-domain knowledge graphs, such as DBpedia [10] and Freebase [11], and also built various knowledge graphs around domain application [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Boosting Knowledge Representation Learning in Entity Alignment through Triple Confidence

Zhang,

Chen,

Wang

2024

Mathematics

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Entity alignment is an important task in knowledge fusion, which aims to link entities that have the same real-world identity in two knowledge graphs. However, in the process of constructing a knowledge graph, some noise may inevitably be introduced, which must affect the results of the entity alignment tasks. The triple confidence calculation can quantify the correctness of the triples to reduce the impact of the noise on entity alignment. Therefore, we designed a method to calculate the confidence of the triples and applied it to the knowledge representation learning phase of entity alignment. The method calculates the triple confidence based on the pairing rates of the three angles between the entities and relations. Specifically, the method uses the pairing rates of the three angles as features, which are then fed into a feedforward neural network for training to obtain the triple confidence. Moreover, we introduced the triple confidence into the knowledge representation learning methods to improve their performance in entity alignment. For the graph neural network-based method GCN, we considered entity confidence when calculating the adjacency matrix, and for the translation-based method TransE, we proposed a strategy to dynamically adjust the margin value in the loss function based on confidence. These two methods were then applied to the entity alignment, and the experimental results demonstrate that compared with the knowledge representation learning methods without integrating confidence, the confidence-based knowledge representation learning methods achieved excellent performance in the entity alignment task.

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Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks

Cited by 30 publications

References 7 publications

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations

A Novel Method for Boosting Knowledge Representation Learning in Entity Alignment through Triple Confidence

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