the movielens datasets: history and context

The MovieLens Datasets:

History and Context

Max Harper (presenter)

Joe Konstan

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http://tiis.acm.org/iui16/

MovieLens: 5 star movie ratings

userId,movieId,rating,timestamp

1,2,3.5,1112486027

1,29,3.5,1112484676

1,32,3.5,1112484819

1,47,3.5,1112484727

1,50,3.5,1112484580

1,112,3.5,1094785740

1,151,4.0,1094785734

1,223,4.0,1112485573

1,253,4.0,1112484940

...

138493,69644,3.0,1260209457

138493,70286,5.0,1258126944

138493,71619,2.5,1255811136

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web site: dataset:

ratings data is interesting, intuitive,

and pervasive

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dataset impact

» 140,000 downloads in 2014

» a search for “movielens” yields

• 6,020 results in Google Books

• 8,920 results in Google Scholar

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dataset uses

» research

» technical: programming books + blogs

» educational (including a MOOC)

» industrial R&D, demos

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overview

» MovieLens datasets overview

» dataset stability, system change

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<user, movie, rating, timestamp>

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<user, movie, rating, timestamp>

<Max, Toy Story, 4.0, 2010-12-01 12:00:00>

MovieLens benchmark datasets

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Name Dates Users Movies Ratings Density

ML 100K ‘97 – ‘98 943 1,682 100,000 6.30%

ML 1M ‘00 – ‘03 6,040 3,706 1,000,209 4.47%

ML 10M ‘95 – ‘09 69,878 10,681 10,000,054 1.34%

ML 20M ‘95 – ‘15 138,493 27,278 20,000,263 0.54%

designed for replicability

MovieLens latest datasets

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Name Dates Users Movies Ratings Density

ML Latest ‘95 – ‘16 247,753 34,208 22,884,377 0.003%

ML Latest

Small‘96 – ‘16 668 10,329 105,339 0.015%

designed for recency

overview

» MovieLens datasets overview

» dataset stability, system change

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tension: datasets vs. system

» ideal (pure) vs. actual (it’s complex)

» systems want to change

• stay current, constant improvements

• A/B tests, beta testing, and other experiments

» context changes

• devices, competing sites, changing user base

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some key changes

» core flow of browse/search

» rating widget

» recommender

» new user experience

» …

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history of experiments

» both online field experiments and online

lab experiments

» created temporary and permanent

changes, changed pattern of use

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in the paper

» the story of MovieLens (1997 origins)

• lessons learned from running a “real” system

in a research lab

• lots of fun descriptive stats/charts

» best practices for dataset researchers

• limitations

• alternatives

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people who made this possible

» John Riedl

» Istvan Albert, Al Borchers, Dan Cosley, Brent J. Dahlen, Rich Davies, Michael Ekstrand, Dan Frankowski, Nathaniel Good, Jon Herlocker, Daniel Kluver, Shyong (Tony) Lam, Michael Ludwig, Sean McNee, Chad Salvatore, Shilad Sen, and Loren Terveen

» MovieLens users

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in ACM Transactions on Interactive Intelligent Systems, Dec. 2015

» feedback? contact us: [email protected]

presented by Max Harper, Research Scientist, University of Minnesota, [email protected]

written with Joe Konstan, Distinguished McKnight University Professor, University of Minnesota, [email protected]

This material is based on work supported by the National Science Foundation under grants DGE-9554517, IIS-9613960, IIS-9734442, IIS-9978717, EIA-9986042, IIS-0102229, IIS-0324851, IIS-0534420, IIS-0808692, IIS-0964695, IIS-0968483, IIS-1017697, IIS-1210863. This project was also supported by the University of Minnesota’s Undergraduate Research Opportunities Program and by grants and/or gifts from Net Perceptions, Inc., CFK Productions, and Google.

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The MovieLens Datasets:

History and Context

mailto:[email protected]



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version 0 (1997) version 4 (2014)

one solution

» document change, include with datasets

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key dataset limitations (1/2)

» system UI and recommender changes

» bias towards “successful” users

» possible bias towards users with tolerance

for “research quality” design

» timestamps do not reflect time of

consumption

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key dataset limitations (2/2)

» recommender systems research

community attitudes

• implicit behaviors > ratings?

• dataset-only research increasingly

discouraged

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MovieLens system evolution

key changes and experiments

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alternative datasets

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Name Domain Rating Scale Ratings Density

Book-

Crossing books 0 - 10 1.1m 0.003%

EachMovie movies 0 - 14 2.7m 2.872%

Jester

(dataset1) jokes -10 - 10 4.1m 57.463%

Amazon many 1 - 5 82.8m < 0.001%

Netflix Prize movies 1 - 5 100.5m 1.178%

Yahoo Music

(C15) music (various) 0 - 100 262.8m 0.042%

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EachMovie

lessons from running MovieLens

» lessons from startups apply (it’s hard, fail

fast)

» continual work, not one-time effort

» encourage code quality through good

social coding conventions

» invest in tools that allow users to help

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dataset uses

» recommender systems research

» recommender systems MOOC• http://coursera.org/learn/recommender-systems

» code examples (popular press, blogs)

» higher education

» commercial – internal testing

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http://coursera.org/learn/recommender-systems