RecVAE

Introduction

[paper]

Title: RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback

Authors: Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey I. Nikolenko

Abstract: Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which used the multinomial likelihood variational autoencoders, has shown excellent results for top-N recommendations. In this work, we propose the Recommender VAE (RecVAE) model that originates from our research on regularization techniques for variational autoencoders. RecVAE introduces several novel ideas to improve Mult-VAE, including a novel composite prior distribution for the latent codes, a new approach to setting the β hyperparameter for the β-VAE framework, and a new approach to training based on alternating updates. In experimental evaluation, we show that RecVAE significantly outperforms previously proposed autoencoder-based models, including Mult-VAE and RaCT, across classical collaborative filtering datasets, and present a detailed ablation study to assess our new developments. Code and models are available at https://github.com/ilya-shenbin/RecVAE.

../../../_images/recvae.png

Running with RecBole

Model Hyper-Parameters:

  • hidden_dimendion (list) : The hidden dimension of auto-encoder. Defaults to 600.

  • latent_dimendion (int) : The latent dimension of auto-encoder. Defaults to 200.

  • dropout_prob (float) : The drop out probability of input. Defaults to 0.5.

  • beta (float) : The default hyperparameter of the weight of KL loss. Defaults to 0.2.

  • gamma (float) : The hyperparameter shared across all users. Defaults to 0.005.

  • mixture_weights (list) : The mixture weights of three composite priors. Defaults to [0.15, 0.75, 0.1].

  • n_enc_epochs (int) : The training times of encoder per epoch. Defaults to 3.

  • n_dec_epochs (int) : The training times of decoder per epoch. Defaults to 1.

  • training_neg_sample (int) : The negative sample num for training. Defaults to 0.

A Running Example:

Write the following code to a python file, such as run.py

from recbole.quick_start import run_recbole

run_recbole(model='RecVAE', dataset='ml-100k')

And then:

python run.py

Note: Because this model is a non-sampling model, so you must set training_neg_sample=0 when you run this model.

Tuning Hyper Parameters

If you want to use HyperTuning to tune hyper parameters of this model, you can copy the following settings and name it as hyper.test.

learning_rate choice [0.01,0.005,0.001,0.0005,0.0001]
latent_dimension choice [64,100,128,150,200,256,300,400,512]

Note that we just provide these hyper parameter ranges for reference only, and we can not guarantee that they are the optimal range of this model.

Then, with the source code of RecBole (you can download it from GitHub), you can run the run_hyper.py to tuning:

python run_hyper.py --model=[model_name] --dataset=[dataset_name] --config_files=[config_files_path] --params_file=hyper.test

For more details about Parameter Tuning, refer to Parameter Tuning.

If you want to change parameters, dataset or evaluation settings, take a look at