lxc86739795 / corr_www

Cooperation of Retriever and Ranker Framework.

CoRR

Here is the code for Cooperative Retriever and Ranker in Deep Recommenders based on recommendation library RecStudio

Dataset

With RecStudio, the dataset can be downloaded automatically by specifying dataset name.

Run

To run CoRR algorithm, you should run:

python run.py -m CoRR -d amazon-electronics --batch_size 512

If you just want to have a try, a tiny dataset is recommended: ml-100k.

For general recommendation, you should run:

python run.py -m CoRRMF -d amazon-electronics --batch_size 1024

The default retriever and ranker are [SASRec, DIN] for sequential recommendation and [MF+DeepFM] for general recommendation. If you want to specify retriever and ranker, run like this:

python run.py -m CoRR -d amazon-electronics --retriever Caser --ranker BST

The default number of negatives is 20, you can specify it with arguments --num_neg, i.e. --num_neg 100.