Difference

For our paper, we adjust some of the pattern.

• Instead of mini-batch which GRU4rec and the original KSR use, We adopt 'data augmentation', the same as SR-GNN, STAMP, etc. (use [0:k] for k in range(1, len(session)) for train, and [k] for target).
• On our dataset, we found that NLL loss performs better than KSR with BPR loss, contrary to the original. Here, we provide NLL codes with BPR codes annotated.
• You need to adjust the dataset path. Ours is "../benchmarks/kg_month15/A/"

Attention

• Without pretraining, you can randomly generate embbeding based on normal distribution (default for item emb). Kg emb pretrained with trans-E, we get a faster loss convergence with metrics a little bit higher . We still use the original code to read local embedding files, you should also generate three embedding files, of which the format is not different from [the original](https://github.com/RUCDM/KSR/blob/master/data/the format of KSR input.txt) .
• To speed up, we begin to valid when epoch > 20; model is saved when it obtains the highest MRR@20. You can alter this in the method "fit".
• As the original version filters out items in test data which not exist in train data (the same way as GRU4rec), we follow it. If you want metrics be divided by the sum of unfiltered test sessions (passed as an arg), you can revise it in evaluation.py.

Input Format

sess data:

The first line is "SessionId,ItemId". Id begins with 0.

SID+','+IID

item_id -> kg_id

ItemId is mapped to entityId in kg for consistency. No header.

IID+'\t'+EID

Args

optional arguments:

-h, --help show this help message and exit

--data DATA dataset folder name under ../benchmarks

--type TYPE our dataset is divided

--epochs EPOCHS Number of epochs.

--batch_size BATCH_SIZE Batch size.

--dropout DROPOUT Dropout rate.

--out_dim OUT_DIM Embedding size of output.

--num_neg NUM_NEG Number of negative instances to pair with a positive instance.

--lr LR Learning rate.

--activation [ACTIVATION] Specify activation function: sigmoid, relu, tanh, identity

--momentum MOMENTUM Momentum as hyperprameter.

--argument use the method called "data argument" if true

--pretrain use kg emb, the pretrained output of OpenKE (trans-E)

--reload restore saved params if true

--eval only eval once, non-train

--save if save model or not

--savepath SAVEPATH for customization

--cuda CUDA gpu No.