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NAVER-AI-RUSH


  • 12 th (total score)

NAVER-AI-RUSH-1 / Image Classification


  • our solution : efficientNet + oct vNet Ensemble

  • 17 th / 100 teams (first round)

  • the period actually participate : Aug 6 to Aug 13 (about 7 days)

NAVER-AI-RUSH-2 / Click-Through Rate (CTR) Prediction


  • our solution : A single CAT model, (only use 7 different features : read_len, read_cnt, total_cnt, read_prob, gender, age_range, hh)

  • 11 th / 30 teams (final round)

  • the period actually participate : Aug 20 to Aug 28 (about 8 days)

  • tried : xDeepFM (feature, article_id, read_len, gender, age_range, hh, image_feature) - didn't go well..

  • tried : Embedding DNN network - didn't see it carefully..

  • tried : xgboost, lgbm for a lot of feature that we could make, (image_feature, catergory_id, cat_in, feature cross, means, std for row numeric values.. etc.. ) - trained well, got better score than our final submit score. But couldn't submit some memory issues, something wrong in features preprocess for test set..

  • Things we couldn't do because of not enough time :

    • submit for image feature, category feature, means, std of numeric cols that we made at training
    • Ensemble different models(xgboost, lgbm and CAT). even couldn't ensemble of result of k-fold of just single CAT, other model, respectively.
    • train item2vec of read articles id (history).. ( i think if we did this and make article_id feature, then we could get very good feature for the model... we just thought but didn't try even though it's not that difficult)