Pytorch implementation for paper: Multi-Intention Oriented Contrastive Learning for Sequential Recommendation (WSDM23).
We implement IOCRec in Pytorch and obtain quite similar results on Toys under the same experimental setting. The default hyper-parameters are set as the optimal values for Toys reported in the paper. Besides, the training log is available for reproduction.
2023-07-07 15:48:05 INFO ------------------------------------------------Best Evaluation------------------------------------------------
2023-07-07 15:48:05 INFO Best Result at Epoch: 33 Early Stop at Patience: 10
2023-07-07 15:48:05 INFO hit@5:0.4513 hit@10:0.5453 hit@20:0.6621 hit@50:0.7935 ndcg@5:0.3588 ndcg@10:0.3891 ndcg@20:0.4186 ndcg@50:0.4455
2023-07-07 15:48:07 INFO -----------------------------------------------------Test Results------------------------------------------------------
2023-07-07 15:48:07 INFO hit@5:0.4022 hit@10:0.5005 hit@20:0.6205 hit@50:0.7594 ndcg@5:0.3145 ndcg@10:0.3462 ndcg@20:0.3765 ndcg@50:0.4048 We provide Toys dataset.
You can run the model with the following code:
python runIOCRec.py --dataset toys --eval_mode uni100 --embed_size 64 --k_intention 4