DGI: Recognition of Textual Entailment via dynamic gate Matching

Xiong, Xi; Li, Yuanyuan; Zhang, Rui; Bu, Zhan; Li, Guiqing; Ju, Shenggen

doi:10.1016/j.knosys.2020.105544

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…e collaborative filtering recommendation combined with the neural network [2] (e.g., CNN, RNN, and CDAE) alleviates this problem. Besides, taking advantage of the productive relationship in the social networks [3][4][5] can effectively solve the cold-start problem [6,7], but there is a malicious fraud problem by distrusting users.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Zhao

Xiong

et al. 2020

Complexity

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Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Zhao

Xiong

et al. 2020

Complexity

Self Cite

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“…Autoregression model is commonly used for forecasting univariate time series and VAR is a generalisation of the AR model for multiple time series and suitable for MIMO forecasting. (d) LSTM : It is a prominent RNN extension with forget gate, input gate, and output gate. LATM is widely used as the baseline of prediction [8, 17, 18, 24, 31]. This method can solve short‐term memory and vanishing gradient problem.…”

Section: Resultsmentioning

confidence: 99%

Forecasting usage and bike distribution of dockless bike‐sharing using journey data

Hua

Chen

et al. 2020

IET intell. transp. syst

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“…First, the infectiousness formula in the FIR layer can be modified to consider not just the quantity of postreplies but also their quality. For instance, natural language analysis [46] could be used to score the quality of posts. Second, the network model can be extended to have more than two layers.…”

Section: Discussionmentioning

confidence: 99%

The Spread of Information in Virtual Communities

Zhang

Meng

et al. 2020

Complexity

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With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.

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DGI: Recognition of Textual Entailment via dynamic gate Matching

Cited by 16 publications

References 4 publications

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Forecasting usage and bike distribution of dockless bike‐sharing using journey data

The Spread of Information in Virtual Communities

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