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.
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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
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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]"
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User features
- CSV file with numeric features, one user per line
- Header row expected, first column should be named
idx
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Item features
- CSV file with numeric features, one item per line
- Header row expected, first column should be named
idx
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Geographical features
- CSV file with numeric features, one item per line
- Header row expected, first column should be named
place_id
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