MyMediaLite

MyMediaLitea lightweight, multi-purpose library of recommender system algorithms

Zeno Gantner

University of Hildesheim

February 5, 2011

Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 1 / 16

Introduction

What are Recommender Systems?


Introduction

MyMediaLite: Recommender System Algorithm Libraryfunctionality

I rating prediction

I item recommendation from implicit feedback

I algorithm testbed

target groups

I recommender system researchers

I educators and students

I application developers

misc info

I written in C#, runs on Mono

I GNU General Public License (GPL)

I regular releases (1 or 2 per month)

why use it?

I simple

I free

I scalable

I well-documented

I choice


Using MyMediaLite

Data Flow

Recommender

Model

interactiondata

user/itemattributes

disk

predictions

hyperparameters


Using MyMediaLite

Methods Implemented in MyMediaLiterating prediction

I averages: global, user, item

I linear baseline method by Koren and Bell

I frequency-weighted Slope One

I k-nearest neighbor (kNN):

I user or item similarities, diff. similarity measuresI collaborative or attribute-/content-based

I (biased) matrix factorization

item prediction from implicit feedback

I random

I most popular item

I linear content-based model optimized for BPR (BPR-Linear)

I support-vector machine using item attributes

I k-nearest neighbor (kNN)

I weighted regularized matrix factorization (WR-MF)

I matrix factorization optimized for BPR (BPR-MF)Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 5 / 16

Using MyMediaLite

Command-Line Tools

I one for each task: rating prediction, item recommendation

I simple text format: CSV

I pick method and parameters using command-line arguments

I evaluate, store/load models

http://ismll.de/mymedialite/documentation/command_line.html


http://ismll.de/mymedialite/documentation/command_line.html

Using MyMediaLite

Embedding MyMediaLite: C#us ing System ;us ing MyMediaLite . Data ;us ing MyMediaLite . Eva l ;us ing MyMediaLite . IO ;us ing MyMediaLite . ItemRecommendation ;

pub l i c c l a s s Example{

pub l i c s t a t i c vo id Main ( s t r i n g [ ] a r g s ){

// l oad the datavar use r mapp ing = new Ent i tyMapp ing ( ) ;var i tem mapping = new Ent i tyMapp ing ( ) ;var t r a i n i n g d a t a = ItemRecommenderData . Read ( a r g s [ 0 ] , user mapping , i tem mapping ) ;var r e l e v a n t i t em s = item mapping . I n t e r n a l I D s ;var t e s t d a t a = ItemRecommenderData . Read ( a r g s [ 1 ] , user mapping , i tem mapping ) ;

// s e t up the recommendervar recommender = new MostPopular ( ) ;recommender . S e tCo l l a b o r a t i v eDa t a ( t r a i n i n g d a t a ) ;recommender . Tra in ( ) ;

// measure the accu racy on the t e s t data s e tvar r e s u l t s = I t emP r e d i c t i o nE v a l . Eva l ua t e ( recommender , t e s t d a t a , t r a i n i n g d a t a ,

r e l e v a n t i t em s ) ;Conso l e . Wr i t eL i n e ( "prec@5 ={0}" , r e s u l t s [ "prec5" ] ) ;

// make a p r e d i c t i o n f o r a c e r t a i n u s e r and i temConso l e . Wr i t eL i n e ( recommender . P r e d i c t ( use r mapp ing . To I n t e r n a l ID (1 ) ,

i tem mapping . To I n t e r n a l ID ( 1 ) ) ) ;}

}


Using MyMediaLite

Embedding MyMediaLite: Python

#!/ u s r / b i n / env i p y

import clr

clr . AddReference ( "MyMediaLite.dll" )from MyMediaLite import ∗

# load the datauser_mapping = Data . EntityMapping ( )item_mapping = Data . EntityMapping ( )train_data = IO . ItemRecommenderData . Read ( "u1.base" , user_mapping , item_mapping )relevant_items = item_mapping . InternalIDstest_data = IO . ItemRecommenderData . Read ( "u1.test" , user_mapping , item_mapping )

# s e t up the recommenderrecommender = ItemRecommendation . MostPopular ( )recommender . SetCollaborativeData ( train_data ) ;recommender . Train ( )

# measure the accu racy on the t e s t data s e tprint Eval . ItemPredictionEval . Evaluate ( recommender , test_data , train_data , relevant_items )

# make a p r e d i c t i o n f o r a c e r t a i n u s e r and i temprint recommender . Predict ( user_mapping . ToInternalID ( 1 ) , item_mapping . ToInternalID ( 1 ) )


Using MyMediaLite

Embedding MyMediaLite: Ruby#!/ u s r / b i n / env i r

require ’MyMediaLite ’

min_rating = 1max_rating = 5

# load the datauser_mapping = MyMediaLite : : Data : : EntityMapping . new ( )item_mapping = MyMediaLite : : Data : : EntityMapping . new ( )

train_data = MyMediaLite : : IO : : RatingPredictionData . Read ( "u1.base" , min_rating , max_rating ,user_mapping , item_mapping )

test_data = MyMediaLite : : IO : : RatingPredictionData . Read ( "u1.test" , min_rating , max_rating ,user_mapping , item_mapping )

# s e t up the recommenderrecommender = MyMediaLite : : RatingPrediction : : UserItemBaseline . new ( )recommender . MinRating = min_rating

recommender . MaxRating = max_rating

recommender . Ratings = train_data

recommender . Train ( )

# measure the accu racy on the t e s t data s e teval_results = MyMediaLite : : Eval : : RatingEval : : Evaluate ( recommender , test_data )eval_results . each do | entry |

puts "#{ entry}"

end

# make a p r e d i c t i o n f o r a c e r t a i n u s e r and i temputs recommender . Predict ( user_mapping . ToInternalID ( 1 ) , item_mapping . ToInternalID ( 1 ) )


Extending MyMediaLite

Roll Your Own Recommendation Method

It’s easy.

for basic functionality

I define model data structures

I write Train() method

I write Predict() method

That’s all!



Roll Your Own: Define Model Data Structures



Roll Your Own: Write Train() Method



Roll Your Own: Write Predict() Method



Roll Your Own Recommendation Method

It’s easy.

You do not need to worry about including the new method to thecommand-line tools, reflection takes care of that.

advanced functionality

I CanPredict() method

I load/store models

I on-line updates


Conclusion

MyMediaLitefuture work

I more methods (contributions welcome . . . )

I additional scenarios: context-aware recommendation, tags, . . .

I distributed/parallel computing

Methods now shipped with MyMediaLite wereused in the MyMedia field trials (>50,000 users).

acknowledgements

I authors: Zeno Gantner, Steffen Rendle, Christoph Freudenthaler

I funding by EC FP7 project “Dynamic Personalization of Multimedia”(MyMedia) under grant agreement no. 215006.

I feedback, patches, suggestions: Thorsten Angermann, Fu Changhong,Andreas Hoffmann, Artus Krohn-Grimberghe, Christina Lichtenthaler,Damir Logar, Thai-Nghe Nguyen


Conclusion

MyMediaLite

homepage: http://ismll.de/mymedialite

fork it: http://gitorious.org/mymedialite

follow us: http://twitter.com/mymedialite

send feedback/patches: [email protected]

MyMediaLite: simple — free — scalable — well-documented


http://ismll.de/mymedialite

http://gitorious.org/mymedialite

http://twitter.com/mymedialite

Date post:	27-Jan-2015
Category:	Technology
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