Caser

Introduction

[paper]

Title: Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Authors: Jiaxi Tang, Ke Wang

Abstract: Top-N sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-N ranked items that a user will likely interact in a “near future”. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (Caser) as a solution to address this requirement. The idea is to embed a sequence of recent items into an “image” in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public data sets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.

../../../_images/caser.png

Running with RecBole

Model Hyper-Parameters:

  • embedding_size (int) : The embedding size of users and items. Defaults to 64.

  • n_h (int) : The number of horizontal Convolutional filters. Defaults to 16.

  • n_v (int) : The number of vertical Convolutional filters. Defaults to 8.

  • reg_weight (float) : The L2 regularization weight. Defaults to 1e-4.

  • dropout_prob (float) : The dropout rate. Defaults to 0.4.

  • loss_type (str) : The type of loss function. If it set to 'CE', the training task is regarded as a multi-classification task and the target item is the ground truth. In this way, negative sampling is not needed. If it set to 'BPR', the training task will be optimized in the pair-wise way, which maximize the difference between positive item and negative item. In this way, negative sampling is necessary, such as setting --neg_sampling="{'uniform':1}". Defaults to 'CE'. Range in ['BPR', 'CE'].

A Running Example:

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

from recbole.quick_start import run_recbole

parameter_dict = {
   'neg_sampling': None,
}
run_recbole(model='Caser', dataset='ml-100k', config_dict=parameter_dict)

And then:

python run.py

Notes:

  • By setting reproducibility=False, the training speed of Caser can be greatly accelerated.

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]
reg_weight choice [0,1e-4,1e-5]
dropout_prob choice [0.0,0.1,0.2,0.3,0.4,0.5]

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