Contrasting Offline and Online Results when Evaluating Recommendation Algorithms

RecSys Boston,Sept17,2016 1

Contrasting Offline and Online Results when Evaluating

Recommendation AlgorithmsMarcoRossettiTrainline Ltd.,London(previouslyUniversityofMilan-Bicocca)

FabioStellaDepartmentofInformatics,SystemsandCommunicationUniversityofMilano-Bicocca

MarkusZankerFacultyofComputerScienceFreeUniversityofBozen-Bolzano

RecSys Boston,Sept17,2016 2

Research Goal

• Given the dominance of offline evaluation reflecting on its validity becomes important

• Said and Bellogin (RecSys 2014) identified serious problems with the internal validity (not reproducible results with different open source frameworks).

• Different results from offline and online evaluations have also been identified putting question marks on the external validity (e.g. Cremonesi et al. 2012, Beel et al. 2013, Garcin et al. 2014, Ekstrand et al. 2014, Maksai et al., 2015).

• Proposition:• Compare performance of an offline experimentation with an online

evaluation.• Use of a within-users experimental design, where we can test for

differences in paired samples.

RecSys Boston,Sept17,2016 3

Research Questions

1. Does the relative ranking of algorithms based on offline accuracy measurements predict the relative ranking according to an accuracy measurement in a user-centric evaluation?

2. Does the relative ranking of algorithms based on offline measurements of the predictive accuracy for long- tail items produce comparable results to a user-centric evaluation?

3. Do offline accuracy measurements allow to predict the utility of recommendations in a user-centric evaluation?

RecSys Boston,Sept17,2016 4

Study Design

• Collected likes on ML moviesfrom 241 users

• On average 137 ratings per user

1

• Same users, evaluated 4 algorithms, 5 recommendations each

• On average 17.4 + 2 recommendations• 122 users returned, 100 after cleaning

2

RecSys Boston,Sept17,2016 5

Offline and Online Evaluations

ML1M

All-but-1validation UsersAnswers

Popularity

MF80:MatrixFactorizationwith80factors

MF400:MatrixFactorizationwith400factors

I2I:ItemToItemK-NearestNeighbors

train

Offlineevaluation Onlineevaluation

Metrics

à precision on all items ß

à precision on long tail ß

useful recommendations ß

RecSys Boston,Sept17,2016 6

Precision All Items

MF400 MF80

POP I2I

p = 0.05 p = 0.05 p = 0.05

MF80 MF400

POP I2I

p = 0.05 p = 0.05 p = 0.1

Algorithm Offline OnlineI2I 0.438 0.546

MF80 0.504 0.598MF400 0.454 0.604POP 0.340 0.516

Offlineprecisionallitems

Onlineprecisionallitems

RecSys Boston,Sept17,2016 7

Precision on Long Tail Items

MF80

MF400

POP

I2I

p = 0.05p = 0.05

p = 0.05

p = 0.05

p = 0.05

p = 0.05

Offline=Onlineprecisionlongtailitems

Algorithm Offline Online

I2I 0.280 0.356MF80 0.018 0.054MF400 0.360 0.628POP 0.000 0.000

RecSys Boston,Sept17,2016 8

Useful Recommendations

MF400I2I

POP

p = 0.05 p = 0.05MF80

p = 0.05 p = 0.05

p = 0.05

Usefulrecommendations

Algorithm OnlineI2I 0.126

MF80 0.082MF400 0.116POP 0.026

RecSys Boston,Sept17,2016 9

Conclusions

• Comparison of different algorithms online and offline based on a within-users experimental design.

• The algorithm performing best according to a traditional offline accuracy measurement was significantly worse, when it comes to useful (i.e. relevant and novel) recommendations measured online.

• Academia and industry should keep investigating this topic in order to find the best possible way to validate offline evaluations.

RecSys Boston,Sept17,2016

Thank you!

10

MarcoRossettiTrainline Ltd.,London@ross85