Predicting user's multi-interests with network embedding in health-related topics

Zhipeng Jin, Ruoran Liu, Qiudan Li, Daniel D. Zeng, Yong Cheng Zhan, Lei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

With the rapid growth of Web 2.0, social media has become a prevalent information sharing and seeking channel for health surveillance, in which users form interactive networks by posting and replying messages, providing and rating reviews, attending multiple discussion boards on health-related topics. Users' behaviors in these interactive networks reflect users' multiple interests. To provide better information service for users, it is necessary to analyze the user interactions and predict users' multi-interests. Most existing work in predicting users' multi-interests based on multi label network classification focuses on using approximate inference methods to leverage the dependency information to improve classification results. Inspired by deep learning techniques, DEEPWALK learns label independent latent representations of vertices in a network using local information obtained from truncated random walks, which provides an efficient way for predicting users multi-interests from user interactions. In this paper, we develop a user's multi-interests prediction model based on DEEPWALK, weight information of user interactions is considered when modeling a stream of short constrained random walks and SkipGram is employed to generate more accurate representations of user vertices, which help identify users' interests. Experimental results on two real world health-related datasets show the efficacy of the proposed model.

Original languageEnglish (US)
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2568-2575
Number of pages8
ISBN (Electronic)9781509006199
DOIs
StatePublished - Oct 31 2016
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: Jul 24 2016Jul 29 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Other

Other2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period7/24/167/29/16

Keywords

  • DEEPWALK
  • Multi-interests prediction
  • User interaction network
  • Weight information

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Jin, Z., Liu, R., Li, Q., Zeng, D. D., Zhan, Y. C., & Wang, L. (2016). Predicting user's multi-interests with network embedding in health-related topics. In 2016 International Joint Conference on Neural Networks, IJCNN 2016 (pp. 2568-2575). [7727520] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2016.7727520