Hypergraph Contrastive Collaborative Filtering (2204.12200v2)

Abstract: Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, so as to comprehensively capture the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. Our model implementation codes are available at https://github.com/akaxlh/HCCF.

Hypergraph Contrastive Collaborative Filtering: An Overview

The paper "Hypergraph Contrastive Collaborative Filtering" introduces a novel framework designed to enhance Collaborative Filtering (CF) paradigms, particularly in addressing two prevalent challenges: over-smoothing in Graph Neural Network (GNN)-based CF architectures, and scarcity of supervisory signals due to sparse user-item interactions. This paper proposes a framework named Hypergraph Contrastive Collaborative Filtering (HCCF) which aligns self-supervised learning with hypergraph structures to improve recommendation systems.

Graph-based Collaborative Filtering Challenges

Traditional GNN-based CF methods, while advanced, often suffer from over-smoothing, a problem where increasing the number of layers leads to homogeneous representations for different nodes and subsequent degradation of recommendation quality. Additionally, these models presume robust, consistent interaction datasets, which is rarely the case in real-world systems where user interactions are both sparse and skewed.

The Proposed Framework: HCCF

HCCF tackles these limitations by employing a hypergraph structure to capture both local and global collaborative patterns. The hypergraph structure learning aspect enhances GNNs' ability to differentiate user preferences thereby mitigating over-smoothing effects. Here are the key components of HCCF as described in the paper:

1. Local Embedding Propagation: The local collaborative relations between users and items are encoded using a simplified GCN approach, which aggregates information from nearby nodes while reducing training complexity.
2. Global Hypergraph Structure Learning: A hypergraph neural network is employed to capture global dependencies between users and items. Unlike classical graph methods that only consider binary user-item relationships, hypergraphs encapsulate multi-relational data providing a broader contextual understanding.
3. Cross-View Contrastive Learning: HCCF features a contrastive learning component where two views—local collaborative relations and global hypergraph dependencies—jointly supervise learning. This compensation is pivotal in refining user-item representations using sparse data. The benefit stems from training the model to maximize mutual information across these two perspectives without resorting to noisy augmentations.

Strong Numerical Validation

The experimental section clearly shows that HCCF outperforms existing state-of-the-art methods across multiple datasets, including Yelp, Movielens, and Amazon-book. Notably, HCCF demonstrated a significant increase in NDCG and Recall metrics, registering an average improvement of almost 20% over LightGCN and 30% over DHCF, attesting to its robustness against sparse data and superior learning capacity in high-order correlations.

Implications and Future Directions

The practical implications of this research are substantial. By enhancing the representation quality, HCCF can potentially increase the personalization in recommendation systems across diverse application domains. Theoretically, the integration of hypergraph structures with self-supervised learning opens new paths for utilizing complex network interactions to address data sparsity and overfitting challenges.

Future developments may explore the incorporation of temporal dynamics or extend the hypergraph framework to other domains such as social network analysis or knowledge graph enhancements. The code being publically available ensures tangible contributions to the research community, fostering further innovation in AI-driven recommender systems.

In conclusion, the insights consolidated in this paper provide a substantial methodological enhancement to CF paradigms, making it a valuable resource for researchers pursuing novel architectures in recommendation systems underpinned by hypergraph theory and contrastive learning.