Transfer learning with deep manifold regularized auto-encoders

Zhu, Yi; Wu, Xindong; Li, Peipei; Zhang, Yuhong; Hu, Xuegang

doi:10.1016/j.neucom.2019.08.078

Cited by 25 publications

(14 citation statements)

References 24 publications

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“…Compared to supervised deep learning methods, there are far less attention paid in autoencoder model for recommendation. However, autoencoder has proven to able to learn substantial representations in many fields, such as image classification [20]. Along this line, we propose to introduce an autoencoder in recommendation systems and achieve satisfied results.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Collaborative Recommendation via Dual-Autoencoder

Dong

Zhu

et al. 2020

IEEE Access

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With the rapid increase of internet information, personalized recommendation systems are an effective way to alleviate the information overload problem, which has attracted extensive attention in recent years. The traditional collaborative filtering utilizes matrix factorization methods to learn hidden feature representations of users and/or items. With deep learning achieved good performance in representation learning, the autoencoder model is widely applied in recommendation systems for the advantages of fast convergence and no label requirement. However, the previous recommendation systems may take the reconstruction output of an autoencoder as the prediction of missing values directly, which may deteriorate their performance and cause unsatisfactory results of recommendation. In addition, the parameters of an autoencoder need to be pre-trained ahead, which greatly increases the time complexity. To address these problems, in this paper, we propose a Hybrid Collaborative Recommendation method via Dual-Autoencoder (HCRDa). More specifically, firstly, a novel dual-autoencoder is utilized to simultaneously learn the feature representations of users and items in our HCRDa, which obviously reduces time complexity. Secondly, embedding matrix factorization into the training process of the autoencoder further improves the quality of hidden features for users and items. Finally, additional attributes of users and items are utilized to alleviate the cold start problem and to make hybrid recommendations. Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed method in comparison with several state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Hybrid Collaborative Recommendation via Dual-Autoencoder

Dong

Zhu

et al. 2020

IEEE Access

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“…Digital maps can present these local regions in different colors according to their indexes. Usually, congested, slow, and smooth are colored, respectively, as red, orange, and green [6]. Digital maps keep the traffic spatial information in nature, and their updating can provide continuous temporal information.…”

Section: Complexitymentioning

confidence: 99%

“…A stacked autoencoder can learn generic traffic flow features, which is the first time that a deep architecture model applies in representing such features [40]. A recurrent convolutional neural network [6] models the nonlinear relationship of adjacent road traffic. An extended deep belief network (DBN) [41] can do better exploitation in data with high nonlinearities and strong correlations.…”

Section: Complexitymentioning

confidence: 99%

“…e autoencoder, proposed by Hinton et al as a generative model [47], shows excellent performance in nonlinear manifold learning [6]. is neural model projects data from high dimension to low dimension [48] and makes sure the extracted features are robust even in learning multiple modalities [49].…”

Section: Complexitymentioning

confidence: 99%

“…Besides, tracking those changes in traffic patterns helps to assess the impacts of city construction planning and policy implementation [5], especially for peak hours. Once there are some traffic restriction policies, drivers may change their routes [6] or commuting mode [7] during peak hours. From citywide perspective, drivers' decisions will bring changes in the distribution of traffic flows in the network and thus induce pattern fluctuation.…”

Section: Introductionmentioning

confidence: 99%

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How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In‐Depth Neural Network Approach with Digital Maps

Zhang

Гонг

et al. 2021

Complexity

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The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital maps keep spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity.

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