Conclusion

1. + § Pervasive Data Access Group, Pennsylvania State University {+pzy102, §wlee}@cse.psu.edu # * Hewlett Packard Labs in China {#ping.luo, *min.wang6}@hp.com 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 • For merchandise • Find proper advertisement slot Ranking Potentially Popular Items from Early VotesPeifeng Yin+ Ping Luo# Min Wang* Wang-Chien Lee § Problem Formulation Evaluation Metrics Popular Item Popular Item • Root mean squared error • Normalized discount cumulative gain Unpopular Item Unpopular Item Conclusion Conformer Observation Experiment Early vote vs. popularity Individual prediction General Comparison No. of Early Votes Maverick Item 1 Item 1’ Item 2 Item 2’ Popularity Item 3 Item 3’ Future Work Item n Item k’ • 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 621-630, 2010. • J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, pages 230-237, 1999. • Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD, pages 426-434, 2008.