Difference between revisions of "Does Wikipedia Information Help Netflix Predictions"
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+ | {{Infobox work | ||
+ | | title = Does Wikipedia Information Help Netflix Predictions | ||
+ | | date = 2008 | ||
+ | | authors = [[John Lees-Miller]]<br />[[Fraser Anderson]]<br />[[Bret Hoehn]]<br />[[Russell Greiner]] | ||
+ | | doi = 10.1109/ICMLA.2008.121 | ||
+ | | link = http://www.ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04724995 | ||
+ | | plink = https://www.researchgate.net/profile/John_Lees-Miller/publication/224362843_Does_Wikipedia_Information_Help_Netflix_Predictions/links/00b7d5165be157a915000000.pdf | ||
+ | }} | ||
'''Does Wikipedia Information Help Netflix Predictions''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[John Lees-Miller]], [[Fraser Anderson]], [[Bret Hoehn]] and [[Russell Greiner]]. | '''Does Wikipedia Information Help Netflix Predictions''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[John Lees-Miller]], [[Fraser Anderson]], [[Bret Hoehn]] and [[Russell Greiner]]. | ||
== Overview == | == Overview == | ||
Authors explore several ways to estimate movie similarity from the free encyclopedia [[Wikipedia]] with the goal of improving predictions for the Netflix Prize. Authors system first uses the content and hyperlink structure of Wikipedia articles to identify similarities between movies. Authors then predict a user's unknown ratings by using these similarities in conjunction with the user's known ratings to initialize matrix factorization and k-Nearest Neighbours algorithms. Authors blend these results with existing ratings-based predictors. Finally, authors discuss empirical results, which suggest that external Wikipedia data does not significantly improve the overall prediction accuracy. | Authors explore several ways to estimate movie similarity from the free encyclopedia [[Wikipedia]] with the goal of improving predictions for the Netflix Prize. Authors system first uses the content and hyperlink structure of Wikipedia articles to identify similarities between movies. Authors then predict a user's unknown ratings by using these similarities in conjunction with the user's known ratings to initialize matrix factorization and k-Nearest Neighbours algorithms. Authors blend these results with existing ratings-based predictors. Finally, authors discuss empirical results, which suggest that external Wikipedia data does not significantly improve the overall prediction accuracy. |
Revision as of 23:00, 5 July 2019
Authors | John Lees-Miller Fraser Anderson Bret Hoehn Russell Greiner |
---|---|
Publication date | 2008 |
DOI | 10.1109/ICMLA.2008.121 |
Links | Original Preprint |
Does Wikipedia Information Help Netflix Predictions - scientific work related to Wikipedia quality published in 2008, written by John Lees-Miller, Fraser Anderson, Bret Hoehn and Russell Greiner.
Overview
Authors explore several ways to estimate movie similarity from the free encyclopedia Wikipedia with the goal of improving predictions for the Netflix Prize. Authors system first uses the content and hyperlink structure of Wikipedia articles to identify similarities between movies. Authors then predict a user's unknown ratings by using these similarities in conjunction with the user's known ratings to initialize matrix factorization and k-Nearest Neighbours algorithms. Authors blend these results with existing ratings-based predictors. Finally, authors discuss empirical results, which suggest that external Wikipedia data does not significantly improve the overall prediction accuracy.