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

Peifeng Yin, Ping Luo, Min Wang, Wang Chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationWSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining
Pages623-632
Number of pages10
DOIs
StatePublished - Mar 15 2012
Event5th ACM International Conference on Web Search and Data Mining, WSDM 2012 - Seattle, WA, United States
Duration: Feb 8 2012Feb 12 2012

Publication series

NameWSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining

Other

Other5th ACM International Conference on Web Search and Data Mining, WSDM 2012
CountryUnited States
CitySeattle, WA
Period2/8/122/12/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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    Yin, P., Luo, P., Wang, M., & Lee, W. C. (2012). A straw shows which way the wind blows: Ranking potentially popular items from early votes. In WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining (pp. 623-632). (WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining). https://doi.org/10.1145/2124295.2124370