Title: Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

Authors: Zihan Lin, Changxin Tian, Yupeng Hou, Wayne Xin Zhao

Abstract: Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users’ preference over items by modeling the user-item interaction graphs. Despite the effectiveness, these methods suffer from data sparsity in real scenarios. In order to reduce the influence of data sparsity, contrastive learning is adopted in graph collaborative filtering for enhancing the performance. However, these methods typically construct the contrastive pairs by random sampling, which neglect the neighboring relations among users (or items) and fail to fully exploit the potential of contrastive learning for recommendation.

To tackle the above issue, we propose a novel contrastive learning approach, named Neighborhood-enriched Contrastive Learning, named NCL, which explicitly incorporates the potential neighbors into contrastive pairs. Specifically, we introduce the neighbors of a user (or an item) from graph structure and semantic space respectively. For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs. In implementation, the representations of users (or items) and neighbors correspond to the outputs of different GNN layers. Furthermore, to excavate the potential neighbor relation in semantic space, we assume that users with similar representations are within the semantic neighborhood, and incorporate these semantic neighbors into the prototype-contrastive objective. The proposed NCL can be optimized with EM algorithm and generalized to apply to graph collaborative filtering methods. Extensive experiments on five public datasets demonstrate the effectiveness of the proposed NCL, notably with 26% and 17% performance gain over a competitive graph collaborative filtering base model on the Yelp and Amazon-book datasets, respectively. Our implementation code is available at:


Running with RecBole

Model Hyper-Parameters:

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

  • n_layers (int) : The number of GNN layers. Defaults to 3.

  • reg_weight (float) : The L2 regularization weight. Defaults to 1e-04.

  • ssl_temp (float) : Temperature for contrastive loss. Defaults to 0.1.

  • ssl_reg (float) : The structure-contrastive loss weight. Defaults to 1e-7.

  • hyper_layers (int) : Controlling the contrasted range for structural-contrastive loss, e.g., when set to 1, the GNN outputs for one user/item of layer 0 and 2 will be regarded as positive pairs. Defaults to 1.

  • alpha (float) : The weight to balance self-supervised loss for users and items. Defaults to 1.0.

  • proto_reg (float) : The prototype-contrastive weight. Defaults to 8e-8.

  • num_clusters (int) : Number of prototypes. Defaults to 1000.

  • m_step (int) : Number of M-steps for each E-step. Defaults to 1.

  • warm_up_step (int) Number of warm up steps. Defaults to 20.

A Running Example:

Write the following code to a python file, such as

from recbole.quick_start import run_recbole

run_recbole(model='NCL', 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.

ssl_temp choice [0.05,0.07,0.1]
ssl_reg choice [1e-6,1e-7]
proto_reg choice [1e-6,1e-7,1e-8]
num_clusters choice [100,1000]

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