Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

By Zihan Lin, Changxin Tian, Yupeng Hou, Wayne Xin Zhao. The Web Conference, 2022

645 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/3fe92dae73a1d98a52a73c1c46a5be3dafd0c14d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Li-zhen Cui
6. Chao Huang
7. Lianghao Xia
8. Zi-Liang Huang

In Information Systems

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Li-zhen Cui
6. Chao Huang
7. Zi-Liang Huang
8. Lianghao Xia

In Transportation

1. Zi-Liang Huang
2. Hongzhi Yin

Citations in the corpus

• LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023, methodology): “To work on identifying semantic neighbors of nodes (users and items), HCCF (Xia et al., 2022b) and NCL (Lin et al., 2022) are introduced to pursue consistent representations between the structurally adjacent nodes and semantic neighbors.”
• Self-Supervised Learning for Recommender Systems: A Survey (2022, methodology): “Feature Clustering proposed by [77], [78], [79] combines CL with clustering, assuming the existence of prototypes in the feature/representation space, and each user/item representation should be semantically similar to assigned prototypes where the prototypes are learned through unsupervised learnin”
• XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (2022, methodology): “(cid:2) NCL [18] is a very recent contrastive model which designs a prototypical contrastive objective to capture the correlations between a user/item and its context.”