FELRec

PyTorch implementation of the FELRec paper.

ABSTRACT

Recommender systems suffer from the cold-start problem whenever a new user joins the platform or a new item is added to the catalog. To address item cold-start, we propose to replace the embedding layer in sequential recommenders with a dynamic storage that has no learnable weights and can keep an arbitrary number of representations. In this paper, we present FELRec, a large embedding network that refines the existing representations of users and items in a recursive manner, as new information becomes available. In contrast to similar approaches, our model represents new users and items without side information or time-consuming fine-tuning. During item cold-start, our method outperforms similar method by 29.50%-47.45%. Further, our proposed model generalizes well to previously unseen datasets.

EXPERIMENTS

You can run our experiments with the short commands below. For a full overview of the parameters, refer to the script files.

Training scripts automatically download the original datasets.

To train FELRec-Q (contrastive learning), run:

python -m trainer.FELRec \
  --job-dir jobs/felrec-q \
  --dataset data/ml-25m \
  --use-queue

You can train FELRec-P (similarity loss) if you remove the use-queue flag:

python -m trainer.FELRec \
  --job-dir jobs/felrec-p \
  --dataset data/ml-25m

To train BPR-MF, run:

python -m trainer.MF \
  --job-dir jobs/mf \
  --dataset data/ml-25m

To train GRU4Rec, run:

python -m trainer.GRU4Rec \
  --job-dir jobs/gru4rec \
  --dataset data/ml-25m

You can train GRU4Rec-BPTT, the BPTT version of GRU4Rec, if you specify the length of session:

python -m trainer.GRU4Rec \
  --job-dir jobs/gru4rec-bptt \
  --dataset data/ml-25m \
  --session 64

To train SASRec, run:

python -m trainer.SASRec \
  --job-dir jobs/sasrec \
  --dataset data/ml-25m

To train JODIE, run:

python -m trainer.JODIE \
  --job-dir jobs/jodie \
  --dataset data/ml-25m