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2012
ACM

A straw shows which way the wind blows: ranking potentially popular items from early votes

6 years 11 months ago
A straw shows which way the wind blows: ranking potentially popular items from early votes
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.
Peifeng Yin, Ping Luo, Min Wang, Wang-Chien Lee
Added 25 Apr 2012
Updated 25 Apr 2012
Type Journal
Year 2012
Where WSDM
Authors Peifeng Yin, Ping Luo, Min Wang, Wang-Chien Lee
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