TY - GEN
T1 - Topic-graph based recommendation on social tagging systems
T2 - 2018 International Conference on Data Science and Information Technology, DSIT 2018
AU - Chen, Yuyun
AU - Dong, Hang
AU - Wang, Wei
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/20
Y1 - 2018/7/20
N2 - Social Tagging Systems (STSs), allowing users to annotate online resources with freely chosen key words, are an essential type of application in Web 2.0. Recommendation in STSs can prevent information overload and support users to locate relevant items for interaction. This article applies a Topic-Graph Based Recommendation approach. First, we discover semantics behind tags through topic inferencing with Latent Dirichlet Allocation (LDA). Second, we conduct Graph-Based Recommendation for tags and users. The approach is applied on a real-word representative data sample collected from the Academic Social Networking Site ResearchGate. The widely used Co-occurrence Based Graph Recommendation is implemented as a baseline approach. Our preliminary human evaluation shows that the Topic-Graph Based Recommendation can complement to the Co-occurrence baseline to provide more reliable results. Future studies are provided on leveraging future features and information for recommendation from researcher-generated social media data on a large scale.
AB - Social Tagging Systems (STSs), allowing users to annotate online resources with freely chosen key words, are an essential type of application in Web 2.0. Recommendation in STSs can prevent information overload and support users to locate relevant items for interaction. This article applies a Topic-Graph Based Recommendation approach. First, we discover semantics behind tags through topic inferencing with Latent Dirichlet Allocation (LDA). Second, we conduct Graph-Based Recommendation for tags and users. The approach is applied on a real-word representative data sample collected from the Academic Social Networking Site ResearchGate. The widely used Co-occurrence Based Graph Recommendation is implemented as a baseline approach. Our preliminary human evaluation shows that the Topic-Graph Based Recommendation can complement to the Co-occurrence baseline to provide more reliable results. Future studies are provided on leveraging future features and information for recommendation from researcher-generated social media data on a large scale.
KW - Academic Social Networking Sites
KW - Data mining
KW - Graph-based recommendation
KW - Probabilistic Topic Models
KW - Social Tagging Systems
UR - http://www.scopus.com/inward/record.url?scp=85055679614&partnerID=8YFLogxK
U2 - 10.1145/3239283.3239316
DO - 10.1145/3239283.3239316
M3 - Conference Proceeding
AN - SCOPUS:85055679614
T3 - ACM International Conference Proceeding Series
SP - 138
EP - 143
BT - Proceedings of the 2018 International Conference on Data Science and Information Technology, DSIT 2018
PB - Association for Computing Machinery
Y2 - 20 July 2018 through 22 July 2018
ER -