We implement our code in Tensorflow and the code is tested under a server with 40-core Intel Xeon E5-2630 v4 @ 2.20GHz CPU, 256G RAM and Nvidia GTX 1080 GPUs (with TensorFlow 1.13 and Python 3).
The preprocessed datasets are in the data directory (e.g. data/ml1m.txt). Each line of the txt format data contains
a user id and an item id, where both user id and item id are indexed from 1 consecutively. Each line represents one interaction between the user
and the item. For every user, their interactions were sorted by timestamp.
The training of the SSE-PT model is handled by the main.py script that provides the following command line arguments.
--dataset STR Name of dataset. Default is "ml1m".
--train_dir STR Train directory. Default is "default".
--batch_size INT Batch size. Default is 128.
--lr FLOAT Learning rate. Default is 0.001.
--maxlen INT Maxmum length of sequence. Default is 50.
--user_hidden_units INT Hidden units of user. Default is 50.
--item_hidden_units INT Hidden units of item. Default is 50.
--num_blocks INT Number of blocks. Default is 2.
--num_epochs INT Number of epochs to run. Default is 2001.
--num_heads INT Number of heads. Default is 1.
--dropout_rate FLOAT Dropout rate value. Default is 0.5.
--threshold_user FLOAT SSE probability of user. Default is 1.0.
--threshold_item FLOAT SSE probability of item. Default is 1.0.
--l2_emb FLOAT L2 regularization value. Default is 0.0.
--gpu INT Name of GPU to use. Default is 0.
--print_freq INT Print frequency of evaluation. Default is 10.
--k INT Top k for NDCG and Hits. Default is 10.
To train our model on the default ml1m data with default parameters:
python3 main.py
To train a SSE-PT model on ml1m data using a maxlen of 200, a dropout rate of 0.2, a SSE probability of 0.08 for user side
and a SSE probability of 0.9 for item side.
python3 main.py --maxlen=200 --dropout_rate 0.2 --threshold_user 0.08 --threshold_item 0.9
The following is the plot of NDCG@10 versus training time (Seconds) for SASRec, SSE-PT and SSE-PT++. Our proposed SSE-PT and SSE-PT++ outperform SASRec.
We based our codes on SASRec. The main regularization method we used is SEE.