KDD Cup 2013Author – Paper Identification

Challenge (2nd place team)

Dmitry EfimovLucas Silva

Benjamin Solecki

Approach summaryGoal: find incorrectlyassigned pairsauthor-paper

Supervisedmachine learning problem

with binary response

Deep feature engineering(> 300 features)

GradientBoosting Machine

(package gbm in R)

Author – Paper graph

Author features

countjournals tf-idf

measure

Count featuresNLP features

Multiplesource features

author’sduplicates

Paper features

Count features NLP features

Multiplesourcefeatures

Additional features

countkeywords

tf-idfmeasure

paper’sduplicates

reversefeatures

engineering

Author – paper features (1 of 4)

Count features

Multiplesourcefeatures

Additionalfeatures

Likelihoodfeatures

Author – paper features (2 of 4)

Count features

Additionalfeatures

count of coauthors

with the same affiliation

reverse feature

engineering: year ranking

feature

Author – paper features (3 of 4)

Multiplesourcefeatures

how many times pair author-paper appeared in the

Microsoft database?

Author – paper features (4 of 4)Likelihood

featuresuse Lj and Lja

as features BAD IDEA

1) use (α∙ Lj + (1−α)∙ Lja) as feature (shrunken likelihood); 2) mixed-effects models (package lme4 in R) to find α

Lj – likelihood by journalLja – likelihood by journal and author

ModelGradient Boosting Machine(package gbm in R)

Grid search for the setof parameters

83 features in the final model (out of 300 calculated features )

Result and conclusion

• Our MAP score is 0.98144 (the winning submission score is 0.98259).

• Many algorithms (LambdaRank, LambdaMART, RankBoost) based on MAP optimization gave less MAP score than GBM with Bernoulli distribution.

• The idea of feature classification based on bipartite author-paper graph is very promising. Analyzing of graph topology can give ideas for new features.

Thank you!