TY - GEN
T1 - A Topic and Concept Integrated Model for Thread Recommendation in Online Health Communities
AU - Li, Mingda
AU - Gao, Weiting
AU - Chen, Yi
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - 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.
AB - 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.
KW - discussion forum
KW - latent dirichlet allocation
KW - neural network
KW - online health community
KW - recommender systems
KW - thread recommendation
UR - http://www.scopus.com/inward/record.url?scp=85095864766&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095864766&partnerID=8YFLogxK
U2 - 10.1145/3340531.3411933
DO - 10.1145/3340531.3411933
M3 - Conference contribution
AN - SCOPUS:85095864766
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 765
EP - 774
BT - CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Y2 - 19 October 2020 through 23 October 2020
ER -