Research on Electricity Consumption Behavior of Users Based on Deep Learning

Wang, Haomiao; He, Huan; Chen, Zhimin; Mahato, Nawaraj Kumar; An, Xiaonan; Gong, Gangjun

doi:10.1109/icpsasia48933.2020.9208392

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Zhong et al [19] proposed multiple aspect attentive graph neural networks to extract user social network features, which can be used to generate user geographic information tag. Wang et al [20] proposed a hybrid model based on deep belief network (DBN) and extreme learning machine (ELM) to analyze users' electricity consumption behavior.…”

Section: Related Workmentioning

confidence: 99%

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Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

et al. 2021

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User tag suggestion technique, aiming at learning users' preferences over knowledge products from their historical behaviors, plays an important role in generating personalized recommendation in online innovation community. However, most current user tagging solutions only utilize a single kind of behavior to predict a single tag for users, resulting in weak generalization of user profile. In this paper, we propose a multiple time series perceptive network (MTSPN) for user tagging tasks in online innovation community. In particular, MTSPN takes multiple kinds of user behaviors into consideration for collaborative perception purpose, in which multi-scale sequential features are extracted from different sequential behaviors, and a multi-label classification module is built-in the proposed MTSPN model to predict multiple tags for users. Our encouraging experimental results on a real-world dataset collected from "Thingiverse" community validate the superiority of the our MTSPN model over several existing user tagging methods.

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Section: Related Workmentioning

confidence: 99%

“…Finally, the user tag is predicted by logistic regression. • DBN [20]: This method takes one kind of user behavior as the input sequence, and uses multiple hidden layers to extract features, so as to realize the correlation learning of adjacent behaviors. The user tag is predicted by Softmax.…”

Section: B Experimental Setupmentioning

confidence: 99%