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.
Keywords
recommendation - network embedding - topic model - LINE
