A Topic and Concept Integrated Model for Thread Recommendation in Online Health Communities

Abstract

Online health communities (OHCs) provide a popular channel for users to seek information, suggestions and support during their medical treatment and recovery processes. To help users find relevant information easily, we present CLIR, an effective system for recommending relevant discussion threads to users in OHCs. We identify that thread content and user interests can be categorized in two dimensions: topics and concepts. CLIR leverages Latent Dirichlet Allocation model to summarize the topic dimension and uses Convolutional Neural Network to encode the concept dimension. It then builds a thread neural network to capture thread characteristics and builds a user neural network to capture user interests by integrating these two dimensions and their interactions. Finally, it matches the target thread's characteristics with candidate users' interests to make recommendations. Experimental evaluation with multiple OHC datasets demonstrates the performance advantage of CLIR over the state-of-the-art recommender systems on various evaluation metrics.