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CF

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Revision as of 01:02, 30 May 2012 by Xlos (Talk | contribs)
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Collaborative Filtering

  • input.txt
    • (user_id, item_id)
  • map reduce 1 - change input format
    • user_id, Vector(item1, item2...)
    • user_item_list.txt
  • map reduce 2 - minhash clustering
    • minhash_id \t user1, user2, user3 ...
    • clustering.txt
  • map reduce 3 - generate recommendation
    • read clustering.txt and generate map in the memory
      • user1 - list(minhash1, minhash2, minhash3 ...)
      • user2 - list(minhash2, minhash3...)
    • mapper
      • read user_item_list.txt
      • emit each purchase record to each minhash cluster which a user belongs to
    • reducer
      • collect all records and make recommendations
    • output
      • user \t item1, item2 ...
  • map reduce 4 - merge recommend list, sort items based on similarity, and print them

Contents Based Recommender

  • 구매기록 기반 -> item minhash -> item - item similarity에 키워드로 추가
  • contents 기반 -> item kmeans clustering -> clustering 내에서 구매기록 기반으로 item CF 수행한 뒤 추천
  • contents & 구매기록 -> item의 document vector에 item의 minhash 추가 (weight 조절) -> cmeans 수행 -> 구매기록 or contents 기반 유사도를 통해 추천
  • item별 키워드 추출 -> 내가 어떤 키워드를 구매했다. -> 사용자별 키워드를 통해 CF -> 키워드별로 질의하여 n개의 추천셋을 만듦
  • TreeClusteringRecommender
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