MyMediaLite is a lightweight recommender system library written in C# that provides functionality for rating prediction, item recommendation from implicit feedback, and algorithm testing. It is designed to be simple, free, scalable, and well-documented. The library allows users to easily implement their own recommendation methods by defining model data structures and writing train and predict methods.

1. MyMediaLite
a 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

2. Introduction
What are Recommender Systems?
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 2 / 16

3. Introduction
MyMediaLite: Recommender System Algorithm Library
functionality
rating prediction
item recommendation from implicit feedback
algorithm testbed
target groups
why use it?
recommender system researchers
simple
educators and students
free
application developers
scalable
misc info well-documented
written in C#, runs on Mono choice
GNU General Public License (GPL)
regular releases (1 or 2 per month)
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 3 / 16

4. Using MyMediaLite
Data Flow
hyperparameters
Recommender
interaction
data predictions
Model
user/item
attributes
disk
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 4 / 16

5. Using MyMediaLite
Methods Implemented in MyMediaLite
rating prediction
averages: global, user, item
linear baseline method by Koren and Bell
frequency-weighted Slope One
k-nearest neighbor (kNN):
user or item similarities, diff. similarity measures
collaborative or attribute-/content-based
(biased) matrix factorization
item prediction from implicit feedback
random
most popular item
linear content-based model optimized for BPR (BPR-Linear)
support-vector machine using item attributes
k-nearest neighbor (kNN)
weighted regularized matrix factorization (WR-MF)
matrix factorization optimized for BPR (BPR-MF)
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 5 / 16

6. Using MyMediaLite
Command-Line Tools
one for each task: rating prediction, item recommendation
simple text format: CSV
pick method and parameters using command-line arguments
evaluate, store/load models
http://ismll.de/mymedialite/documentation/command_line.html
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 6 / 16

7. Using MyMediaLite
Embedding MyMediaLite: C#
using System ;
using M yM e d ia Lite . Data ;
using M yM e d ia Lite . E v a l ;
using M yM e d ia Lite . IO ;
using M yM e d ia Lite . ItemRecommendation ;
p u b l i c c l a s s Example
{
p u b l i c s t a t i c v o i d Main ( s t r i n g [ ] a r g s )
{
// l o a d t h e d a t a
v a r u s e r m a p p i n g = new E n t i t y M a p p i n g ( ) ;
v a r i t e m m a p p i n g = new E n t i t y M a p p i n g ( ) ;
v a r t r a i n i n g d a t a = ItemRecommenderData . Read ( a r g s [ 0 ] , u s e r m a p p i n g , i t e m m a p p i n g ) ;
var r e l e v a n t i t e m s = item mapping . I n t e r n a l I D s ;
v a r t e s t d a t a = ItemRecommenderData . Read ( a r g s [ 1 ] , u s e r m a p p i n g , i t e m m a p p i n g ) ;
// s e t up t h e recommender
v a r recommender = new M o s t P o p u l a r ( ) ;
recommender . S e t C o l l a b o r a t i v e D a t a ( t r a i n i n g d a t a ) ;
recommender . T r a i n ( ) ;
// m e a s u r e t h e a c c u r a c y on t h e t e s t d a t a s e t
v a r r e s u l t s = I t e m P r e d i c t i o n E v a l . E v a l u a t e ( recommender , t e s t d a t a , t r a i n i n g d a t a ,
relevant items );
C o n s o l e . W r i t e L 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 t e m
C o n s o l e . W r i t e L i n e ( recommender . P r e d i c t ( u s e r m a p p i n g . T o I n t e r n a l I D ( 1 ) ,
item mapping . ToInternalID ( 1 ) ) ) ;
}
}
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 7 / 16

8. Using MyMediaLite
Embedding MyMediaLite: Python
#! / u s r / b i n / e n v i p y
import clr
clr . AddReference ( " MyMediaLite . dll " )
from MyMediaLite import ∗
# load the data
user_mapping = Data . EntityMapping ( )
item_mapping = Data . EntityMapping ( )
train_data = IO . I t e m R ec o m m e n d e r Da t a . Read ( " u1 . base " , user_mapping , item_mapping )
relev ant_ite ms = item_mapping . InternalIDs
test_data = IO . I t e m R ec o m m e n d e r Da t a . Read ( " u1 . test " , user_mapping , item_mapping )
# s e t up t h e recommender
recommender = I te mR e co m me nd a ti o n . MostPopular ( )
recommender . S e t C o l l a b o r a t i v e D a t a ( train_data ) ;
recommender . Train ( )
# m e a s u r e t h e a c c u r a c y on t h e t e s t d a t a s e t
print Eval . I t e m Pr ed i ct i on Ev a l . 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 t e m
print recommender . Predict ( user_mapping . ToInternalID ( 1 ) , item_mapping . ToInternalID ( 1 ) )
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 8 / 16

9. Using MyMediaLite
Embedding MyMediaLite: Ruby
#! / u s r / b i n / e n v i r
require ’ MyMediaLite ’
min_rating = 1
max_rating = 5
# load the data
user_mapping = MyMediaLite : : Data : : EntityMapping . new ( )
item_mapping = MyMediaLite : : Data : : EntityMapping . new ( )
train_data = MyMediaLite : : IO : : R a t i n g P r e d i c t i o n D a t a . Read ( " u1 . base " , min_rating , max_rating ,
user_mapping , item_mapping )
test_data = MyMediaLite : : IO : : R a t i n g P r e d i c t i o n D a t a . Read ( " u1 . test " , min_rating , max_rating ,
user_mapping , item_mapping )
# s e t up t h e recommender
recommender = MyMediaLite : : RatingPrediction : : UserItemBaseline . new ( )
recommender . MinRating = min_rating
recommender . MaxRating = max_rating
recommender . Ratings = train_data
recommender . Train ( )
# m e a s u r e t h e a c c u r a c y on t h e t e s t d a t a s e t
eval_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 t e m
puts recommender . Predict ( user_mapping . ToInternalID ( 1 ) , item_mapping . ToInternalID ( 1 ) )
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 9 / 16

10. Extending MyMediaLite
Roll Your Own Recommendation Method
It’s easy.
for basic functionality
define model data structures
write Train() method
write Predict() method
That’s all!
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 10 / 16

11. Extending MyMediaLite
Roll Your Own: Define Model Data Structures
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 11 / 16

12. Extending MyMediaLite
Roll Your Own: Write Train() Method
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 12 / 16

13. Extending MyMediaLite
Roll Your Own: Write Predict() Method
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 13 / 16

14. Extending MyMediaLite
Roll Your Own Recommendation Method
It’s easy.
You do not need to worry about including the new method to the
command-line tools, reflection takes care of that.
advanced functionality
CanPredict() method
load/store models
on-line updates
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 14 / 16

15. Conclusion
MyMediaLite
future work
more methods (contributions welcome . . . )
additional scenarios: context-aware recommendation, tags, . . .
distributed/parallel computing
Methods now shipped with MyMediaLite were
used in the MyMedia field trials (>50,000 users).
acknowledgements
authors: Zeno Gantner, Steffen Rendle, Christoph Freudenthaler
funding by EC FP7 project “Dynamic Personalization of Multimedia”
(MyMedia) under grant agreement no. 215006.
feedback, patches, suggestions: Thorsten Angermann, Fu Changhong,
Andreas Hoffmann, Artus Krohn-Grimberghe, Christina Lichtenth¨ler,
a
Damir Logar, Thai-Nghe Nguyen
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 15 / 16

16. Conclusion
MyMediaLite
homepage: http://ismll.de/mymedialite
fork it: http://gitorious.org/mymedialite
follow us: http://twitter.com/mymedialite
send feedback/patches: mymedialite@ismll.de
MyMediaLite: simple — free — scalable — well-documented
Zeno Gantner, University of Hildesheim: MyMediaLite Recommender System Library — http://ismll.de/mymedialite 16 / 16