Title: Noise Contrastive Estimation for One-Class Collaborative Filtering

Authors: Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai

Abstract: Previous highly scalable One-Class Collaborative Filtering (OC-CF) methods such as Projected Linear Recommendation (PLRec) have advocated using fast randomized SVD to embed items into a latent space, followed by linear regression methods to learn personalized recommendation models per user. However, naive SVD embedding methods often exhibit a strong popularity bias that prevents them from accurately embedding less popular items, which is exacer- bated by the extreme sparsity of implicit feedback matrices in the OC-CF setting. To address this deficiency, we leverage insights from Noise Contrastive Estimation (NCE) to derive a closed-form, effi- ciently computable “depopularized” embedding. We show that NCE item embeddings combined with a personalized user model from PLRec produces superior recommendations that adequately account for popularity bias. Further analysis of the popularity distribution of recommended items demonstrates that NCE-PLRec uniformly distributes recommendations over the popularity spectrum while other methods exhibit distinct biases towards specific popularity subranges. Empirically, NCE-PLRec produces highly competitive performance with run-times an order of magnitude faster than existing state-of-the-art approaches for OC-CF.

Running with RecBole

Model Hyper-Parameters:

  • rank (int) : The latent dimension of latent representations. Defaults to 450.

  • beta (float) : The popularity sensitivity. Defaults to 1.0.

  • reg_weight (float) : The regularization weight. Defaults to 1e-02.

A Running Example:

Write the following code to a python file, such as

from recbole.quick_start import run_recbole

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

And then:


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.

rank choice [100,200,450]
beta choice [0.8,1.0,1.3]
reg_weight choice [1e-04,1e-02,1e2,15000]

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