Titlebook: Web and Big Data; Second International Yi Cai,Yoshiharu Ishikawa,Jianliang Xu Conference proceedings 2018 Springer Nature Switzerland AG 20

責(zé)問(wèn)

痛打

Multivariate Time Series Clustering via Multi-relational Community Detection in Networksthe ability of networks to characterize both local and global relationship amongst nodes (representing data samples), while the use of MNMF can give full play to complex relations amongst individual series and preserve the multi-way nature of multivariate information. Preliminary experiment indicates promising results of our proposed approach.

Instantaneous

Multivariate Time Series Clustering via Multi-relational Community Detection in Networksthe ability of networks to characterize both local and global relationship amongst nodes (representing data samples), while the use of MNMF can give full play to complex relations amongst individual series and preserve the multi-way nature of multivariate information. Preliminary experiment indicates promising results of our proposed approach.

彎曲道理

Attentive and Collaborative Deep Learning for Recommendationmodel, learning of latent factors of users and items can be facilitated by deep processing of items’ tag information. Furthermore, user preferences learned are interpretable. Experiments conducted on a real world dataset demonstrate that our model can significantly outperform the state-of-the-art deep collaborative filtering models.

NOVA

Attentive and Collaborative Deep Learning for Recommendationmodel, learning of latent factors of users and items can be facilitated by deep processing of items’ tag information. Furthermore, user preferences learned are interpretable. Experiments conducted on a real world dataset demonstrate that our model can significantly outperform the state-of-the-art deep collaborative filtering models.

impaction

記憶法

Sentiment Classification via Supplementary Information Modeling methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.

Lament

減至最低

Sentiment Classification via Supplementary Information Modeling methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.

把手

An Estimation Framework of Node Contribution Based on Diffusion Informationmportance of nodes in the spreading processes. Then, we propose an estimation framework and give the method to estimate node contribution based on diffusion samples. Accordingly, the Contribution Estimation algorithm is proposed upon the framework. Finally, we implement our algorithm and test the efficiency on two weighted social networks.