MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation

Cui, Qiang; Wu, Shu; Liu, Qiang; Zhong, Wen; Wang, Liang

doi:10.1109/tkde.2018.2881260

Cited by 127 publications

(52 citation statements)

References 31 publications

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“…As mentioned in the above example, RNN has been widely used for recommendation [19]- [23], sentiment analysis [24], and text classification [25] because they can capture global long-term dependencies and temporal sentence semantics by the directed cycle connections between units. However, it is known that RNN has gradient vanishing or exploding problems [26], [27].…”

Section: A Reviews-based Recommendationmentioning

confidence: 99%

Aspect-Based Fashion Recommendation With Attention Mechanism

2020

IEEE Access

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With the rapid growth of fashion e-commerce, fashion recommendation has become a main digital marketing tool that is built on customer reviews and ratings. Online review is a powerful source for understanding users' shopping experiences, preferences and feedbacks on product/item performances, and thus is useful for enhancing personalized recommendations for future purchases. However, most extant fashion recommendation methods lack effective frameworks to integrate local and global aspect representations extracted from customers' ratings and reviews. In this paper, we proposed an aspect-based fashion recommendation model with attention mechanism (AFRAM) to predict customer ratings based on online reviews of fashion products. This model can extract latent aspect features about users and items separately through two parallel paths of convolutional neural networks (CNN), long short-term memory networks (LSTM), and attention mechanisms. One path processes user reviews and the other copes with item reviews. On each path, CNN and LAST are both coupled with an attention mechanism to capture local aspect features and global aspect features respectively, which are combined through a mutual operation module. The extracted features from the two paths are further merged through another mutual operation to predict users' ratings. The mutual operations on both paths can enhance the generalization of the AFRAM model. Realworld customer reviews and ratings collected from two renowned business websites were used to train and test AFRAM. The experiment results demonstrate that AFRAM is more effective in customer rating predictions, as compared to several state-of-the-art fashion recommenders.

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Section: A Reviews-based Recommendationmentioning

confidence: 99%

Aspect-Based Fashion Recommendation With Attention Mechanism

2020

IEEE Access

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“…It is impossible to achieve the complete convergence in each training, only to adjust the last several layers of weights. Because the front layer is not adjusted, the loss is not completely eliminated [38]. We can also set the learning rate α which changes with the loss function to prevent the feedback from falling too fast with the gradient and missing the minimum convergence value, or at the same time, we can set the momentum impulse which changes with the α reverse to prevent falling into the local optimal solution.…”

Section: B Lstmmentioning

confidence: 99%

Communication Protocol Classification Based on LSTM and DBN

Dou

2020

IEEE Access

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In the battlefield, we often don't know the parameters about enemy wireless communication system. Therefore, we need to use electronic reconnaissance equipment to search, intercept, identify and analyze enemy wireless communication signal. However, the exciting electronic reconnaissance methods can only detect signal layer parameters such as signal carrier frequency and bandwidth, and cannot obtain more information. In order to improve the reconnaissance ability, we propose a novel communication protocol classification algorithm based on long short-term memory (LSTM) and deep belief network (DBN). We first introduce the DBN, then simulates communication protocol classification method based on DBN. In order to improve the performance, we optimize the method. We uses the LSTM to pre-process the data to extract the feature firstly. Then we make the feature as the input of DBN to classify the communication protocol. Finally we make simulation to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm has very good performance to classify the communication protocol. INDEX TERMS LSTM, DBN, wireless channel, protocol classification.

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“…Chen et al [48] proposed a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Cui et al [49] presented a visual and textural recurrent neural network (VT-RNN), which simultaneously learned the sequential latent vectors of user's interest and captured the content-based representations.…”

Section: Deep Learning For Recommender Systemsmentioning

confidence: 99%

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

Gan

Zhang

2020

Complexity

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Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade. Learning effective latent factors plays the most important role in recommendation methods. Several recent works extracted latent factors from user-generated content such as ratings and reviews and suffered from the sparsity problem and the unbalanced distribution problem. To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata. In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items. Experiments conducted on the Amazon product data demonstrate that our approach outperforms multiple state-of-the-art baselines. We provide further insight into the design selections and hyperparameters of our recommendation method. In addition, we further explore the relative importance of various item metadata information on improving the rating prediction performance towards personalized product recommendation, which is extremely valuable for feature extraction in practice.

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MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation

Cited by 127 publications

References 31 publications

Aspect-Based Fashion Recommendation With Attention Mechanism

Aspect-Based Fashion Recommendation With Attention Mechanism

Communication Protocol Classification Based on LSTM and DBN

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

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