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TransFM

This repository contains an implementation of TransFM, as described in the paper:

Rajiv Pasricha, Julian McAuley, "Translation-based Factorization Machines for Sequential Recommendation", RecSys 2018.

This repository also includes implementations of vanilla FMs, as well as the proposed PRME-FM and HRM-FM models.

Please cite the paper above if you use or extend our models.

File formats

  • Input dataset

    • One example per line
    • <user_id> <item_id> <rating> <timestamp>
    • Values separated by a space
    • No header row
    • Example row: User_12 Item_65 5.0 1376697600
  • Item categories

    • CSV file, one item per line
    • Expected header: item_id,item_cat_seq
    • item_cat_seq: comma separated list of item category IDs, enclosed as a string.
    • Example row: 2643,"[165, 193, 442]"
  • User features

    • CSV file with numeric features, one user per line
    • Header row expected, first column should be named idx
  • Item features

    • CSV file with numeric features, one item per line
    • Header row expected, first column should be named idx
  • Geographical features

    • CSV file with numeric features, one item per line
    • Header row expected, first column should be named place_id

Example command

python main.py \
      --filename ratings_Automotive.txt.gz
      --model TransFM
      --features categories
      --features_file item_cat_seq_Automotive.csv.gz
      --max_iters 1000000
      --num_dims 10
      --linear_reg 10.0
      --emb_reg 1.0
      --trans_reg 0.1
      --init_mean 0.1
      --starting_lr 0.02
      --lr_decay_factor 1.0
      --lr_decay_freq 1000
      --eval_freq 50
      --quit_delta 1000

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