Title: Learning Disentangled Representations for Recommendation

Authors: Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu

Abstract: User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users’ decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user’s preference when executing an intention. Learning representations that uncover and disentangle these latent factors can bring enhanced robustness, interpretability, and controllability. However, learning such disentangled representations from user behavior is challenging, and remains largely neglected by the existing literature. In this paper, we present the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled representations from user behavior. Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e.g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately. A micro-disentanglement regularizer, stemming from an information-theoretic interpretation of VAEs, then forces each dimension of the representations to independently reflect an isolated low-level factor (e.g., the size or the color of a shirt). Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines. We further demonstrate that the learned representations are interpretable and controllable, which can potentially lead to a new paradigm for recommendation where users a


Running with RecBole

Model Hyper-Parameters:

  • embedding_size (int) : The latent dimension of auto-encoder. Defaults to 128.

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

  • kfac (int) : Number of facets (macro concepts). 10.

  • nogb (boolean) : Disable Gumbel-Softmax sampling. False.

  • std (float) : Standard deviation of the Gaussian prior. False.

  • encoder_hidden_size (list) : The MLP hidden layer. Defaults to [600].

  • tau (float) : Temperature of sigmoid/softmax, in (0,oo). False.

  • anneal_cap (float) : The super parameter of the weight of KL loss. Defaults to 0.2.

  • total_anneal_steps (int) : The maximum steps of anneal update. Defaults to 200000.

  • reg_weights (list) : L2 regularization. Defaults to [0.0,0.0].

  • 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

from recbole.quick_start import run_recbole

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

And then:


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]
kafc choice [3,5,10,20]

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 to tuning:

python --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