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: https://github.com/RUCAIBox/NCL.
Running with RecBole¶
embedding_size (int): The embedding size of users and items. Defaults to
n_layers (int): The number of GNN layers. Defaults to
reg_weight (float): The L2 regularization weight. Defaults to
ssl_temp (float): Temperature for contrastive loss. Defaults to
ssl_reg (float): The structure-contrastive loss weight. Defaults to
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
alpha (float): The weight to balance self-supervised loss for users and items. Defaults to
proto_reg (float): The prototype-contrastive weight. Defaults to
num_clusters (int): Number of prototypes. Defaults to
m_step (int): Number of M-steps for each E-step. Defaults to
warm_up_step (int)Number of warm up steps. Defaults to
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='NCL', dataset='ml-100k')
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
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
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