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Jobandtalent at recsys challenge 2016

  1. JOBANDTALENT AT RECSYS CHALLENGE 2016
  2. 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
  3. 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
  4. 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.
  5. RECSYS CHALLENGE 2016 COLLECTORS ▸ Freshness. ▸ Item popularity. ▸ User behavior.
  6. 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.
  7. 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 %
  8. SEE YOU THE NEXT YEAR!
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