A Personalized Recommendation System based on Knowledge Graph Embedding and Neural Network

Wang, Penghua; Li, Xiaoge; Du, Feihong; Liu, Huan; Zhi, Shuting

doi:10.1109/icdsba48748.2019.00042

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The model generates word vectors that capture the semantic information of the words. Nowadays, the technique is not limited to the NLP domain, but the algorithm is also used in the recommender system domain [7]. We propose a model based on pre-trained user and item embeddings, called PNCF, to solve the item recommendation problem.…”

Section: Introductionmentioning

confidence: 99%

PNCF: neural collaborative filtering based on pre-trained embedding

Pan

Yamamura

Yoshikawa

2022

International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022)

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In this paper, we propose the recommendation algorithm PNCF for neural networks. We designed a pre-training task for a distributed representation of embeddings based on many-to-many information. We used the word2vec technique in natural language processing to implement the embedding of users and items. We also constructed a brand-new video website tagauthor pre-training dataset. The code in this paper was implemented in PyTorch and is publicly available on GitHub (github.com/jannchie/ PNCF).

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Section: Introductionmentioning

confidence: 99%

PNCF: neural collaborative filtering based on pre-trained embedding

Pan

Yamamura

Yoshikawa

2022

International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022)

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Trust enhanced POI recommendation with collaborative learning

Wu,

Xu,

Rui

et al. 2024

IDA

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With the rapid development and popularization of smart mobile devices, users tend to share their visited points-of-interest (POIs) on the network with attached location information, which forms a location-based social network (LBSN). LBSNs contain a wealth of valuable information, including the geographical coordinates of POIs and the social connections among users. Nowadays, lots of trust-enhanced approaches have fused the trust relationships of users together with other auxiliary information to provide more accurate recommendations. However, in the traditional trust-aware approaches, the embedding processes of the information on different graphs with different properties (e.g., user-user graph is an isomorphic graph, user-POI graph is a heterogeneous graph) are independent of each other and different embedding information is directly fused together without guidance, which limits their performance. More effective information fusion strategies are needed to improve the performance of trust-enhanced recommendation. To this end, we propose a Trust Enhanced POI recommendation approach with Collaborative Learning (TECL) to merge geographic information and social influence. Our proposed model integrates two modules, a GAT-based graph autoencoder as trust relationships embedding module and a multi-layer deep neural network as a user-POI graph learning module. By applying collaborative learning strategy, these two modules can interact with each other. The trust embedding module can guide the selection of user’s potential features, and in turn the user-POI graph learning module enhances the embedding process of trust relationships. Different information is fused through the two-way interaction of information, instead of travelling in one direction. Extensive experiments are conducted using real-world datasets, and results illustrate that our suggested approach outperforms state-of-the-art methods.

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Knowledge Graph and GNN-Based News Recommendation Algorithm With Edge Computing Support

Yao

Zhao

2022

International Journal of Distributed Systems and Technologies

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The current news information from different media websites has posed a serious problem, i.e., it is very difficult to obtain the satisfactory news contents from the measured data information. There have been some researches on news recommendation to improve the experience of users. In spite of this, they always need the further improvement because the news information has showed the explosive increasing way. Therefore, this paper studies knowledge graph and graph neural network (GNN) based news recommendation algorithm with edge computing consideration. At first, the knowledge graph is used for the knowledge extraction. Then, GNN is used to train the extracted features to complete the news recommendation algorithm. Finally, the edge computing is used to offload the high volumes of traffic to the edge server for the news recommendation computation. Compared with two baselines, the proposed algorithm is more efficient, increasing accuracy rate by 2.73% and 9.94% respectively, and decreasing response time by 84.27% and 87.58 respectively.

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A Personalized Recommendation System based on Knowledge Graph Embedding and Neural Network

Cited by 3 publications

References 9 publications

PNCF: neural collaborative filtering based on pre-trained embedding

PNCF: neural collaborative filtering based on pre-trained embedding

Trust enhanced POI recommendation with collaborative learning

Knowledge Graph and GNN-Based News Recommendation Algorithm With Edge Computing Support

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