markov {raincin} | R Documentation |
Google's PageRank algorithm for Rating and Ranking
Description
Calculate ratings and provide rankings using Google's PageRank algorithm
Usage
markov(
jpMat,
method = "markovvl",
dampingFactor = 0.85,
ties.method = "average"
)
Arguments
jpMat |
a Judge-Presenter matrix, or a User-Movie matrix |
method |
a character string specifying Markov's method, including "markov", "markovvl", "markovlvpd", "markovwlvp". |
dampingFactor |
the PageRank theory holds that an imaginary surfer who is randomly clicking on links will eventually stop clicking. The probability, at any step, that the person will continue is a damping factor. Web 0.85, NFL 0.60, NCAA basketball 0.50 |
ties.method |
a character string specifying how ties are treated, including "average", "first", "last", "random", "max", "min", from base::rank |
Details
-
markov
: Markov's method, voting with losses, equivalent tomarkovvl
-
markovvl
: Markov's method, voting with losses -
markovlvpd
: Markov's method, losers vote with point differentials -
markovwlvp
: Markov's method, winners and losers vote with points
Author(s)
Jiangtao Gou
References
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30, 107-117. Proceedings of the Seventh International World Wide Web Conference.
Gou, J. and Wu, S. (2020). A Judging System for Project Showcase: Rating and Ranking with Incomplete Information. Technical Report.
Langville, A. N. and Meyer, C. D. (2012). Who's Number 1?: The Science of Rating and Ranking. Princeton University Press.
Examples
jpMat <- matrix(data=c(5,4,3,0, 5,5,3,1, 0,0,0,5, 0,0,2,0, 4,0,0,3, 1,0,0,4),
nrow=6,
byrow=TRUE)
result <- markov(jpMat,
method='markovvl',
dampingFactor=0.85,
ties.method='average')
print(result)