TY - GEN
T1 - A straw shows which way the wind blows
T2 - 5th ACM International Conference on Web Search and Data Mining, WSDM 2012
AU - Yin, Peifeng
AU - Luo, Ping
AU - Wang, Min
AU - Lee, Wang Chien
PY - 2012
Y1 - 2012
N2 - Prediction of popular items in online content sharing systems has recently attracted a lot of attention due to the tremendous need of users and its commercial values. Different from previous works that make prediction by fitting a popularity growth model, we tackle this problem by exploiting the latent conforming and maverick personalities of those who vote to assess the quality of on-line items. We argue that the former personality prompts a user to cast her vote conforming to the majority of the service community while on the contrary the later personality makes her vote different from the community. We thus propose a Conformer-Maverick (CM) model to simulate the voting process and use it to rank top-k potentially popular items based on the early votes they received. Through an extensive experimental evaluation, we validate our ideas and find that our proposed CM model achieves better performance than baseline solutions, especially for smaller k.
AB - Prediction of popular items in online content sharing systems has recently attracted a lot of attention due to the tremendous need of users and its commercial values. Different from previous works that make prediction by fitting a popularity growth model, we tackle this problem by exploiting the latent conforming and maverick personalities of those who vote to assess the quality of on-line items. We argue that the former personality prompts a user to cast her vote conforming to the majority of the service community while on the contrary the later personality makes her vote different from the community. We thus propose a Conformer-Maverick (CM) model to simulate the voting process and use it to rank top-k potentially popular items based on the early votes they received. Through an extensive experimental evaluation, we validate our ideas and find that our proposed CM model achieves better performance than baseline solutions, especially for smaller k.
UR - http://www.scopus.com/inward/record.url?scp=84863281404&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863281404&partnerID=8YFLogxK
U2 - 10.1145/2124295.2124370
DO - 10.1145/2124295.2124370
M3 - Conference contribution
AN - SCOPUS:84863281404
SN - 9781450307475
T3 - WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining
SP - 623
EP - 632
BT - WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining
Y2 - 8 February 2012 through 12 February 2012
ER -