Collaborative RNN

This is a TensorFlow implementation of the Collaborative RNN presented in the paper

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems, Young-Jun Ko, Lucas Maystre, Matthias Grossglauser, ACML, 2016.

A PDF of the paper can be found here.

Requirements

The code is tested with

• Python 2.7.12 and 3.5.1
• NumPy 1.13.3
• TensorFlow 1.4.0
• CUDA 8.0
• cuDNN 6.0
• six 1.11.0

If you are interested in quickly testing out our code, you might want to check out our step-by-step guide for running the collaborative RNN on an AWS EC2 p2.xlarge instance.

Quickstart

Reproducing the results of the paper should be as easy as following these three steps.

1. Download the datasets.

   • The last.fm dataset is available on Òscar Celma's page. The relevant file is userid-timestamp-artid-artname-traid-traname.tsv.
   • The BrighKite dataset is available at SNAP. The relevant file is loc-brightkite_totalCheckins.txt.

2. Preprocess the data (relabel user and items, remove degenerate cases, split into training and validation sets). This can be done using the script utils/preprocess.py. For example, for BrightKite:

    python utils/preprocess.py brightkite path/to/raw_file.txt
   

   This will create two files named brightkite-train.txt and brightkite-valid.txt.

3. Run crnn.py on the preprocessed data. For example for BrightKite, you might want to try running

    python -u crnn.py brightkite-{train,valid}.txt --hidden-size=32 \
        --learning-rate=0.0075 --rho=0.997 \
        --chunk-size=64 --batch-size=20 --num-epochs=25
   

Here is a table that summarizes the settings that gave us the results published in the paper. All the setting can be passed as command line arguments to crnn.py.

On a modern server with an Nvidia Titan X (Maxwell generation) GPU it takes around 40 seconds per epoch for the BrightKite dataset, and around 14 minutes per epoch on the last.fm dataset.