Methods Inf Med 2021; 60(05/06): 133-146
DOI: 10.1055/s-0041-1736462
Original Article

Integrating Topic Model and Network Embedding for Thread Recommendation

Wei Wei
1   School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, People's Republic of China
,
Rui Wang
2   Ctrip.com International Ltd, Shanghai, People's Republic of China
› Institutsangaben

Abstract

Objectives A thread is the most common information aggregation unit in a health forum, so effective thread recommendation is critical for improving the user experience in an online health community (OHC). This paper proposes an OHC thread recommendation method based on topic model and network embedding, which recommends threads to users by training a classifier and predicting user reply behavior.

Methods The proposed model uses the network structure to describe valid information in OHCs and treats a recommendation as the task of predicting links between users and threads in the network. Topic nodes are added to the information network to better represent the features of users and threads. The results of the latent Dirichlet allocation (LDA) model describe thread topics and user interests from the perspectives of consumer health vocabulary in OHCs and social support types. The large-scale information network embedding technology LINE is used to mine the node's contextual information from the network structure to obtain the low-dimensional vectors of nodes. We optimize the representation method and similarity calculation of network nodes and enrich the network structure information contained in the recommended features to improve the recommendation effect.

Results To verify the proposed model, we collected data from the diabetes forum “Sweet Home.” The experimental results show that the proposed model can effectively extract user interests in threads from the information network and optimize thread recommendation in OHCs.



Publikationsverlauf

Eingereicht: 17. Januar 2021

Angenommen: 29. August 2021

Publikationsdatum:
22. Oktober 2021 (online)

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