Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

Jiang, Dazhi; Liu, Zhichao; Zheng, Lin; Chen, Jianwei

doi:10.1109/access.2020.2968297

Cited by 15 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: E Discussionmentioning

confidence: 99%

An Audio Data Representation for Traffic Acoustic Scene Recognition

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…We also plan to further improve our model by considering the concept change n the traffic flow patterns, and further evaluate our model on other forecasting problems. Furthermore, the proposed model can be easily extended to other temporal related applications, such as multi-dimensional time series prediction [52], [53], prediction of stable bitstream [54], [55], recommendation systems [56] or temporal human emotion computing [57], etc.…”

Section: ) K-nearest Neighbor (Knn)mentioning

confidence: 99%

A Sample-Rebalanced Outlier-Rejected $k$ -Nearest Neighbor Regression Model for Short-Term Traffic Flow Forecasting

Cai

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Short-term traffic flow forecasting is a fundamental and challenging task due to the stochastic dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a sample-rebalanced and outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting. In this model, we adopt a new metric for the evolutionary traffic flow patterns, and reconstruct balanced training sets by relative transformation to tackle the imbalance issue. Then, we design a hybrid model that considers both local and global information to address the limited size of the training samples. We employ four realworld benchmark datasets often used in such tasks to evaluate our model. Experimental results show that our model outperforms state-of-the-art parametric and non-parametric models. INDEX TERMS Intelligent transportation systems, road transportation, time series analysis, stochastic processes.

show abstract

“…There is an inextricable relationship between neural networks and MF models [4], [22]. For example, Neural Collaborative Filtering (NCF) [23] generalizes MF from the perspective of neural networks to achieve CF.…”

Section: B Neural-topic Collaborative Filteringmentioning

confidence: 99%

“…The purpose of CTM is to make full use of the semantic information on topic modeling and the learning ability of factorization models [2], [3]. Compared to factorization models, neural networks have a stronger ability to learn and generalize [4], [5]. Therefore, the fusion of neural networks and topic models, called Neural-Topic Collaborative Filtering (NTCF), can fully utilize and optimize topic models by using neural network technology [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

Using pre-trained topic information to assist in training neural networks can effectively support the completion of the rating prediction task. However, existing neural-topic methods consider only the use of topic information corresponding to current users and items without neighbors, whereas existing memory-based neighborhood approaches are inappropriate for the direct modeling of neighbors with topics. To address the limitations, we argue that memory networks have the ability to organize neighbors with corresponding topics well and can provide a general solution to this problem. To confirm our hypothesis, we propose two approaches. One is an augmented memory network to couple with and enhance existing neural-topic models. The other is a symmetric memory network activated by a memory reorganization mechanism, which is a compact and generalized method for rating prediction. The experimental results demonstrate the effectiveness of the memory reorganization mechanism and show that the two proposed methods have advantages over existing state-of-the-art topic modeling approaches.

show abstract

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

Cited by 15 publications

References 35 publications

An Audio Data Representation for Traffic Acoustic Scene Recognition

An Audio Data Representation for Traffic Acoustic Scene Recognition

A Sample-Rebalanced Outlier-Rejected $k$ -Nearest Neighbor Regression Model for Short-Term Traffic Flow Forecasting

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

Contact Info

Product

Resources

About