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JOBANDTALENT AT
RECSYS CHALLENGE 2016
RECSYS CHALLENGE 2016
LEARNING TO RANK IN RECSYS 2016
▸ Learning to Rank Framework
▸ Listwise, Pairwise, Pointwise
▸ Relevance Judgements
▸ Training & Evaluation dataset
▸ LambdaMART
▸ Gradient tree boosting & NDCG loss function
RECSYS CHALLENGE 2016
DATA EXPLORATION
▸ Understanding data.
▸ Probability of Item being interacted "Popularity".
▸ Probability of Item being interacted by his terms titles
▸ Probability of recurring interactions.
▸ Probability of a user interacting Items and geographical
distance
RECSYS CHALLENGE 2016
RETRIEVERS
▸ Job title/Job tag match
▸ Relevant items per interaction
▸ Interactions made by user
▸ Impressions shown to user
▸ Collaborative filtering, Item to Item/User to User
▸ Clustered users.
RECSYS CHALLENGE 2016
COLLECTORS
▸ Freshness.
▸ Item popularity.
▸ User behavior.
RECSYS CHALLENGE 2016
LEARNING PROCESS
▸ Speed up the training process.
▸ Reducing the number of features applied.
▸ Reducing the number of elements used to train.
▸ Internal evaluation.
▸ NDCG@30 metric.
RECSYS CHALLENGE 2016
RESULTS & CONCLUSIONS
Run Internal Official Improved
Baseline 22849 30721
Baseline BM25 (b=0.2 / k1=1.2) 23591 36857 20 %
Interactions made by the user 162846 229949 523 %
Training process 176312 241422 5 %
Impressions with decay 400217 434433 79,94 %
Item popularity 435325 475940 10 %
Clustering 442049 488192 3 %
Collaborative filtering 457391 507022 4 %
Best effort @ 2000 480396 533232 5 %
Best effort @ 9000 482083 535899 1 %
SEE YOU THE NEXT YEAR!

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Jobandtalent at recsys challenge 2016

  • 2.
  • 3. RECSYS CHALLENGE 2016 LEARNING TO RANK IN RECSYS 2016 ▸ Learning to Rank Framework ▸ Listwise, Pairwise, Pointwise ▸ Relevance Judgements ▸ Training & Evaluation dataset ▸ LambdaMART ▸ Gradient tree boosting & NDCG loss function
  • 4. RECSYS CHALLENGE 2016 DATA EXPLORATION ▸ Understanding data. ▸ Probability of Item being interacted "Popularity". ▸ Probability of Item being interacted by his terms titles ▸ Probability of recurring interactions. ▸ Probability of a user interacting Items and geographical distance
  • 5. RECSYS CHALLENGE 2016 RETRIEVERS ▸ Job title/Job tag match ▸ Relevant items per interaction ▸ Interactions made by user ▸ Impressions shown to user ▸ Collaborative filtering, Item to Item/User to User ▸ Clustered users.
  • 6. RECSYS CHALLENGE 2016 COLLECTORS ▸ Freshness. ▸ Item popularity. ▸ User behavior.
  • 7. RECSYS CHALLENGE 2016 LEARNING PROCESS ▸ Speed up the training process. ▸ Reducing the number of features applied. ▸ Reducing the number of elements used to train. ▸ Internal evaluation. ▸ NDCG@30 metric.
  • 8. RECSYS CHALLENGE 2016 RESULTS & CONCLUSIONS Run Internal Official Improved Baseline 22849 30721 Baseline BM25 (b=0.2 / k1=1.2) 23591 36857 20 % Interactions made by the user 162846 229949 523 % Training process 176312 241422 5 % Impressions with decay 400217 434433 79,94 % Item popularity 435325 475940 10 % Clustering 442049 488192 3 % Collaborative filtering 457391 507022 4 % Best effort @ 2000 480396 533232 5 % Best effort @ 9000 482083 535899 1 %
  • 9. SEE YOU THE NEXT YEAR!