Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

Zheng, Lin; Guo, Naicheng; Jin, Yu; Jiang, Dazhi

doi:10.1109/access.2020.2991093

Cited by 9 publications

(3 citation statements)

References 25 publications

(58 reference statements)

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“…This reveals the deep neural network can be applied for Internet fraud detection. The deep neural network is general, and can be extended to other applications, such as traffic flow forecasting [25]- [36], recommendation systems [37]- [39], medical image processing [40]- [44], intelligent computing [45]- [50].…”

Section: E Performance Evaluation and Discussionmentioning

confidence: 99%

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Fang

Zhou

et al. 2021

IEEE Access

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Recently, Internet finance is increasingly popular. However, bad debt has become a serious threat to Internet financial companies. The fraud detection models commonly used in conventional financial companies is logistic regression. Although it is interpretable, the accuracy of the logistic regression still remains to be improved. This paper takes a large public loan dataset, e.g. Lending club, for example, to explore the potential of applying deep neural network for fraud detection. We first fill the missing values by a random forest. Then, an XGBoost algorithm is employed to select the most discriminate features. After that, we propose to use a synthetic minority oversampling technique to deal with the sample imbalance. With the preprocessed data, we design a deep neural network for Internet loan fraud detection. Extensive experiments have been conducted to demonstrate the outperformance of the deep neural network compared with the commonly-used models. Such a simple yet effective model may brighten the application of deep learning in anti-fraud for Internet loans, which would benefit the financial engineers in small and medium Internet financial companies.INDEX TERMS Internet finance, loan fraud detection, deep learning, financial model.

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Section: E Performance Evaluation and Discussionmentioning

confidence: 99%

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Fang

Zhou

et al. 2021

IEEE Access

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“…Accurate recognition of the scenes is really relevant for applications with the purpose of context machine awareness, which is of critical importance for intelligent transportation or automatic pilot. The feature extraction method presented is general, and can be extend to other time series analysis tasks, such traffic flow forecasting [31]- [33], intelligent computing [34]- [36], or medical signal visualization [37], [38].…”

Section: E Discussionmentioning

confidence: 99%

An Audio Data Representation for Traffic Acoustic Scene Recognition

et al. 2020

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Acoustic scene recognition (ASR), recognizing acoustic environments given an audio recording of the scene, has a wide range of applications, e.g. robotic navigation and audio forensic. However, ASR remains challenging mainly due to the difficulty of representing audio data. In this paper, we focus on traffic acoustic data. Traffic acoustic sense recognition provides complementary information to visual information of the scene; for example, it can be used to verify the visual perception result. The acoustic analysis and recognition, in consideration of its simple and convenient, can effectively enhance the perception ability which only applies visual information. We propose an audio data representation method to improve the traffic acoustic scene recognition accuracy. The proposed method employs the constant Q transform (CQT) and histogram of gradient (HOG) to transfer the one-dimensional audio signals into a time-frequency representation. We also propose two data representation mechanisms, called global and local feature selections, in order to select features that are able to describe the shape of time-frequency structures. We finally exploit the least absolute shrinkage and selection operator (LASSO) technique to further improve the recognition accuracy, by further selecting the most representative information for the recognition. We implemented extensive experiments, and the results show that the proposed method is effective, significantly outperforming the state-of-the-art methods.

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“…Inspired by GRU4REC [3], MV-RNN [15] incorporates visual and textual information to alleviate the item cold start problem. Furthermore, ROM [16] utilizes an interactive self-attention mechanism to adaptively reorganize the entity memory and the topic memory for the rating prediction task. However, RNN-based methods assume that the adjacent items in a session have a fixed sequential dependence, which may generate wrong dependencies and introduce noises in real-world scenarios like music playing.…”

Section: Recurrent Neural Network Modelsmentioning

confidence: 99%

HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

Guo,

Liu,

et al. 2021

Preprint

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Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR field, graph-based approaches are a relatively powerful kind of way, which generally extract item information by message aggregation under Euclidean space. However, such methods can't effectively extract the hierarchical information contained among consecutive items in a session, which is critical to represent users' preferences. In this paper, we present a hyperbolic contrastive graph recommender (HCGR), a principled session-based recommendation framework involving Lorentz hyperbolic space to adequately capture the coherence and hierarchical representations of the items. Within this framework, we design a novel adaptive hyperbolic attention computation to aggregate the graph message of each user's preference in a session-based behavior sequence. In addition, contrastive learning is leveraged to optimize the item representation by considering the geodesic distance between positive and negative samples in hyperbolic space. Extensive experiments on four real-world datasets demonstrate that HCGR consistently outperforms state-of-the-art baselines by 0.43%-28.84% in terms of HitRate, N DCG and M RR.

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Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

Cited by 9 publications

References 25 publications

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

An Audio Data Representation for Traffic Acoustic Scene Recognition

HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

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