1. Ranking Potentially Popular Items from Early Votes Peifeng Yin +
Ranking Potentially Popular Items from Early Votes Peifeng Yin + Ping Luo # Min Wang * Wang-Chien Lee §
+ § Pervasive Data Access Group, Pennsylvania State University
{+pzy102,
# * Hewlett Packard Labs in China
{#ping.luo,
Why rank potential popularity?
Conformer-Maverick Model
For common people
Find interesting items as early as possible
Avoid “rich-get-richer” awkwardness
Popularity means obsolete, e.g., hot deals
Conformer
Maverick
Popular Item
Unpopular Item
For merchandise
Find proper advertisement slot
Problem Formulation
Item 1
Item 2
Item n
Item 3
Item 1’
Item 2’
Item k’
Item 3’
Popularity
Evaluation Metrics
Root mean squared error
Normalized discount cumulative gain
Observation
Experiment
Early vote vs. popularity
Individual prediction
General Comparison
No. of Early Votes
Conclusion
Future Work
CM model assumes a binary latent personality, Conformer & Maverick for each person.
Potential popularity can be ranked based on the early voters’ personality distribution
Extend the CM model to support multi-value rating, e.g., 1 - 5
Explore the topic-sensitive CM personality distribution, e.g., movie vote
References
G. Szabo and B. o. A. Huberman. Predicting the popularity of online content. Communications of the Acm, 53(8):80-88, 2010.
K. Lerman and T. Hogg. Using a model of social dynamics to predict popularity of news. In WWW, pages , 2010.
J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, pages , 1999.
Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD, pages , 2008.