A Tri-Attention Neural Network Model-BasedRecommendation

Wang, Nanxin; Yang, Linlin; Zheng, Yu; Cai, Xiaoyan; Mei, Xin; Dai, Hang

doi:10.1155/2020/3857871

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…LSTM is an improved RNN (Recurrent Neural Network) model that solves the problems of gradient explosion or gradient disappearance during RNN training. Different from the single tanh loop structure in standard RNN, LSTM is a special network with three "gates" [21,22]. ey are the forget gate, input gate, and output gate.…”

Section: Long-and Short-term Memory Network Layermentioning

confidence: 99%

“…is paper adds an AAM layer [22] to the method, which can better capture the affective information in the movie box office data and grasp the core data information. It overcomes the problem of the standard LSTM model using the same state vector in each step of the prediction, which results in the inability to fully learn the detailed information of the sequence encoding during the prediction.…”

Section: Adaptive Attention Mechanism Layermentioning

confidence: 99%

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Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

Feng

Liu

2020

Complexity

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To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment.

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Section: Long-and Short-term Memory Network Layermentioning

confidence: 99%

Section: Adaptive Attention Mechanism Layermentioning

confidence: 99%

Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

Feng

Liu

2020

Complexity

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Spam review detection with Metapath-aggregated graph convolution network

Jayashree

Laila

Amuthan

2023

IFS

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The large flux of online products in today’s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task.

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A Tri-Attention Neural Network Model-BasedRecommendation

Cited by 2 publications

References 37 publications

Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

Spam review detection with Metapath-aggregated graph convolution network

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