We describe our approach for solution of Author - Paper Identification Challenge: https://www.kaggle.com/c/kdd-cup-2013-author-paper-identification-challenge

1. KDD Cup 2013
Author – Paper Identification
Challenge (2nd place team)
Dmitry Efimov
Lucas Silva
Benjamin Solecki

2. Approach summary
Goal: find incorrectly
assigned pairs
author-paper
Supervised
machine learning problem
with binary response
Deep
feature engineering
(> 300 features)
Gradient
Boosting Machine
(package gbm in R)

3. Author – Paper graph

4. Author features
count
journals tf-idf
measure
Count features
NLP features
Multiple
source
features
author’s
duplicates

5. Paper features
Count features NLP features
Multiple
source
features
Additional
features
count
keywords
tf-idf
measure
paper’s
duplicates
reverse
features
engineering

6. Author – paper features (1 of 4)
Count
features
Multiple
source
features
Additional
features
Likelihood
features

7. Author – paper features (2 of 4)
Count
features
Additional
features
count of
coauthors with
the same
affiliation
reverse feature
engineering:
year ranking
feature

8. Author – paper features (3 of 4)
Multiple
source
features
how many times
pair author-paper
appeared in the
Microsoft database?

9. Author – paper features (4 of 4)
Likelihood
features
use Lj and Lja
as features
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 journal
Lja – likelihood by journal
and author

10. Model
Gradient Boosting Machine
(package gbm in R)
Grid search for the set
of parameters
83 features in the final model
(out of 300 calculated features )

11. 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.

12. Thank you!