User behavior prediction via heterogeneous information preserving network embedding

Yuan, Weiwei; He, Kangya; Han, Guangjie; Guan, Donghai; Khattak, Asad Masood

doi:10.1016/j.future.2018.09.036

Cited by 13 publications

(5 citation statements)

References 22 publications

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“…Wang et al [37] propose SHINE model, which utilizes deep neural network structure to learn the low-dimensional representation of nodes to predict sentiment links between users and celebrities. Yuan et al [38] propose to fuse multi-networks information to learn the nodes' low-dimensional embedding representation for user behavior classification.…”

Section: Network Embeddingmentioning

confidence: 99%

Multiplex network embedding for implicit sentiment analysis

Zhao

Liu

Zhang

2021

Complex Intell. Syst.

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As one of the hot research directions in natural language processing, sentiment analysis has received continuous and extensive attention. Different from explicit sentiment words indicating sentiment polarity, implicit sentiment analysis is a more challenging problem due to the lack of sentiment words, which makes it inadequate to use traditional sentiment analysis method to judge the polarity of implicit sentiment. This paper takes sentiment analysis as a special sign link prediction problem, which is different from traditional text-based method. In particular, by performing the word graph-based text level information embedding and heterogeneous social network information embedding (i.e. user social relationship network embedding, and user-entity sentiment network embedding), the proposed scheme learns the highly nonlinear representations of network nodes, explores early fusion method to combine the strength of these two types of embedding modeling, optimizes all parameters simultaneously and creates enhanced context representations, leading to better capture of implicit sentiment polarity. The proposed method has been examined on real-world dataset, for implicit sentiment link prediction task. The experimental results demonstrate that the proposed method outperforms state-of-the-art schemes, including LINE, node2vec, and SDNE, by 20.2%, 19.8%, and 14.0%, respectively, on accuracy, and achieves at least 14% gains on AUROC. For sentiment analysis accuracy, the proposed method achieves AUROC of 80.6% and accuracy of 78.3%, which is at least 31% better than other models. This work can provide useful guidance on the implicit sentiment analysis.

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Section: Network Embeddingmentioning

confidence: 99%

Multiplex network embedding for implicit sentiment analysis

Zhao

Liu

Zhang

2021

Complex Intell. Syst.

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“…Network representation has become an important way to analyze complex network. The learning methods can be categorized into two types: matrix factorization (MF)-based and neural network-based [18].…”

Section: Related Workmentioning

confidence: 99%

Degree-biased random walk for large-scale network embedding

Zhang

Shi

Feng

et al. 2019

Future Generation Computer Systems

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h i g h l i g h t s• The current random walk based network embedding methods cannot well adapt to the scale-free feature of real-world networks.• A degree-biased random walk method is proposed to adapt to the scale-free feature of real-world networks and extract topological information as fully as possible for representation learning.• The space and computation overhead for network embedding can be drastically reduced by adopting by condensing the fixed-length sequences into variable-length ones based on nodes centrality.

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“…These network applications bring great convenience to people's lives and attract users to evaluate their services, thus generating a huge amount of user evaluation data. On the one hand, it isn't easy to construct user behavior models based on user evaluation data [4][5][6][7]. On the other hand, the performance of data processing will be greatly reduced due to the limitations of the computational capacity and computational methods of traditional data processing systems [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Big Data User Behaviour Prediction Model Incorporating Deep Learning

Huang

2024

Applied Mathematics and Nonlinear Sciences

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With the improvement of the quality of human life, various industries utilize deep learning technology to meet the needs of users. In this paper, after exploring the deep temporal model and deep forest algorithm (DF) model in extracting the characteristics of user behavior features, we propose a user behavior prediction model based on CNN-LSTM and add a front embedding layer as well as a feature fusion layer to improve it and increase the prediction accuracy. The weighted average method is used to integrate CNN-LSTM with DF for algorithmic model fusion, aiming to improve the robustness and stability of the model and achieve accurate predictions of user behavior. In the empirical analysis, the correct rate of the fusion model proposed in this paper exceeds the proper rate of several other models by 11.75-15.73%, and it can reach 11.2% recall at k=20. Meanwhile, the lower the user behavior level is, the higher the prediction accuracy of the CNNLSTM+DF algorithm is, which can reach up to 95.20%. The lower the average relative error and average absolute error are, which can reach 13.45% and 3.92min respectively, which verifies the validity of the fusion model proposed in this paper, and provides a reference for the research in the related fields.

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User behavior prediction via heterogeneous information preserving network embedding

Cited by 13 publications

References 22 publications

Multiplex network embedding for implicit sentiment analysis

Multiplex network embedding for implicit sentiment analysis

Degree-biased random walk for large-scale network embedding

Big Data User Behaviour Prediction Model Incorporating Deep Learning

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