TY - GEN
T1 - Connecting users with similar interests via tag network inference
AU - Wang, Xufei
AU - Liu, Huan
AU - Fan, Wei
PY - 2011
Y1 - 2011
N2 - The popularity of social networking greatly increases interaction among people. However, one major challenge remains - how to connect people who share similar interests. In a social network, the majority of people who share similar interests with given a user are in the long tail that accounts for 80% of total population. Searching for similar users by following links in social network has two limitations: it is inefficient and incomplete. Thus, it is desirable to design new methods to find like-minded people. In this paper, we propose to use collective wisdom from the crowd or tag networks to solve the problem. In a tag network, each node represents a tag as described by some words, and the weight of an undirected edge represents the co-occurrence of two tags. As such, the tag network describes the semantic relationships among tags. In order to connect to other users of similar interests via a tag network, we use diffusion kernels on the tag network to measure the similarity between pairs of tags. The similarity of people's interests are measured on the basis of similar tags they share. To recommend people who are alike, we retrieve top k people sharing the most similar tags. Compared to two baseline methods triadic closure and LSI, the proposed tag network approach achieves 108% and 27% relative improvements on the BlogCatalog dataset, respectively.
AB - The popularity of social networking greatly increases interaction among people. However, one major challenge remains - how to connect people who share similar interests. In a social network, the majority of people who share similar interests with given a user are in the long tail that accounts for 80% of total population. Searching for similar users by following links in social network has two limitations: it is inefficient and incomplete. Thus, it is desirable to design new methods to find like-minded people. In this paper, we propose to use collective wisdom from the crowd or tag networks to solve the problem. In a tag network, each node represents a tag as described by some words, and the weight of an undirected edge represents the co-occurrence of two tags. As such, the tag network describes the semantic relationships among tags. In order to connect to other users of similar interests via a tag network, we use diffusion kernels on the tag network to measure the similarity between pairs of tags. The similarity of people's interests are measured on the basis of similar tags they share. To recommend people who are alike, we retrieve top k people sharing the most similar tags. Compared to two baseline methods triadic closure and LSI, the proposed tag network approach achieves 108% and 27% relative improvements on the BlogCatalog dataset, respectively.
KW - diffusion kernel
KW - like-minded users
KW - tag network
UR - http://www.scopus.com/inward/record.url?scp=83055191248&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83055191248&partnerID=8YFLogxK
U2 - 10.1145/2063576.2063723
DO - 10.1145/2063576.2063723
M3 - Conference contribution
AN - SCOPUS:83055191248
SN - 9781450307178
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1019
EP - 1024
BT - CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
T2 - 20th ACM Conference on Information and Knowledge Management, CIKM'11
Y2 - 24 October 2011 through 28 October 2011
ER -