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cikm-cup-2016-cross-device's Introduction

CIKM CUP 2016 Track 1: Cross-Device Linking

The goal of the competition is to find browsing logs which belong to the same user.

More details at http://cikmcup.org and https://competitions.codalab.org/competitions/11171

My solution

  • Convert user ids into integers so they occupy less RAM
  • Split the train data into 2 folds based on connected components
  • Use Elastic Search and More-Like-This queries to find top pair candidates
  • Split logs into sessions (using 30 minute intervals) and compute the user "profile" (log features):
    • Number of sessions
    • Clicks within session
    • Duration of breaks between sessions
    • Starts and ends of sessions
    • Title-based, Domain-based and Url-based similarities within sessions
  • For candidates retrieved with Elastic Search, compute the following features:
    • Absolute difference between the profile features
    • Cosine between domains, full urls and titles
  • Train an xgboost model for predicting if a candidate pair corresponds to the same user or not

Files:

  • 1_prepare_data.py: preprocesses the data
  • 2_data_to_elastic.py: puts the log data to elastic search
  • 3_candidates_elastic.py: uses elastic search for retrieving top 70 candidates for each user
  • 4_session_vectorizers.py: "trains" count vectorizers for urls, domains and titles for user sessions
  • 5_user_profiles.py: extracts profile information from each user log
  • 6_pair_features.py: computes features for each candidate pair
  • 7_model.py: trains the xgb model and creates the submission file

Presentation

  • This solution was presented at Berlin Machine Learning meetup. See the slides here.

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