Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2202.06200v2)

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. 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 code is available at: https://github.com/RUCAIBox/NCL.

• The paper introduces Neighborhood-enriched Contrastive Learning (NCL) to counteract data sparsity in graph collaborative filtering.
• It leverages both structural and semantic neighbors to construct meaningful contrastive pairs for improved embedding robustness.
• Experimental results show up to 26% improvement on key datasets, highlighting NCL's potential for more accurate recommender systems.

Overview of "Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning"

The paper "Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning" presents a methodological advancement in the domain of graph collaborative filtering (GCF) for recommender systems. The authors identify a key limitation in existing GCF methods, which is their susceptibility to data sparsity issues. To address this, they integrate contrastive learning (CL) to enhance the learnability and robustness of user-item interaction embeddings. The paper introduces a unique framework termed Neighborhood-enriched Contrastive Learning (NCL), which enriches contrastive learning tasks by incorporating neighborhood information, moving beyond naive random sampling used in traditional contrastive learning applications.

Methodological Contribution

The core contribution of NCL lies in its innovative treatment of neighborhood incorporation in constructing contrastive pairs. Previous implementations of contrastive learning in GCF applied random sampling for contrastive pair construction, often overlooking the nuanced relational information provided by user and item neighborhoods. NCL addresses this by explicitly accounting for both structural and semantic neighborhoods, utilizing this richly informative data to create more meaningful contrastive pairs.

1. Structural Neighbors: The authors propose leveraging the structural neighbors of a node within a graph, defined as nodes connected through paths in the interaction graph. These nodes provide direct, often multilayered contextual relationships among users or items.
2. Semantic Neighbors: The paper introduces the concept of semantic neighbors, which are users or items that, although not directly connected, share similar characteristics or user preferences. Incorporation of these neighbors is realized through a prototype-contrastive learning mechanism, which employs clustering techniques to derive latent space representations or prototypes.

Implementation and Optimization

The NCL framework is implemented as a graph neural network (GNN) that processes user-item interactions, and its effectiveness is validated through a series of experiments across five datasets. The authors optimize NCL using an Expectation-Maximization (EM) algorithm for the prototype-contrastive component, which iteratively refines the semantic clusters by maximizing the likelihood of observed representations given latent prototypes.

Experimental Validation

Significant performance improvements were observed when implementing NCL in comparison with traditional GCF methods and recent self-supervised learning techniques. For example, the model exhibited a 26% improvement on the Yelp dataset and a 17% improvement on the Amazon-book dataset over a competitive benchmark. These gains highlight the importance of a detailed architectural consideration of contrastive pair construction in NCL.

Implications and Future Directions

From a practical standpoint, the integration of enriched neighborhood relations in GCF frameworks has substantial implications for improving recommendation accuracy, particularly in data-sparse environments. The explicit modeling of these relations enables more robust embeddings, thereby increasing the generalization capacity of recommendation models.

The paper’s theoretical contributions extend to further deepening our understanding of contrastive learning’s role in recommendation systems, particularly how relational context derived from graph structures can be harnessed to optimize learning objectives.

In future work, it could be beneficial to explore the scalability of NCL across larger datasets or more complex recommendation scenarios. Another intriguing direction might be the dynamic adaptation of neighborhood definitions to capture evolving user behaviors over time.

In summary, the paper advances the field of GCF by offering a compelling approach to counteract data sparsity through a nuanced utilization of contrastively learned neighborhood information, charting a course for more effective recommender systems.