Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System (2204.08807v1)

Abstract: Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (kNN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/MCCLK.

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

The paper "Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System" introduces a novel approach, termed MCCLK, which enhances the performance of knowledge-aware recommender systems through the application of contrastive learning techniques. The authors emphasize utilizing multi-level cross-view contrast mechanisms to tackle inherent challenges in Knowledge Graph (KG) aware recommendation, particularly addressing the sparse supervised signal problem prevalent in Graph Neural Network (GNN)-based models.

Key Contributions

The paper makes several significant contributions to the field of recommendation systems:

1. Multi-view Framework: MCCLK operates under a comprehensive framework that integrates three distinct graph views from the KG: a global-level structural view, a local-level collaborative view, and a semantic view. This multi-view approach enables a richer extraction of collaborative and semantic information essential for recommendation tasks.
2. Cross-view Contrastive Learning: The approach leverages contrastive learning not just within a single view but across multiple views, both globally and locally. By incorporating contrastive learning, MCCLK can effectively harness self-supervised signals from the data itself, addressing the issue of sparse supervised signals.
3. Semantic View with $k$NN Graph Construction: The semantic view is enhanced via a $k$-Nearest-Neighbor item-item semantic graph construction module. This module aims to capture crucial item-item semantic relations that are often overlooked in other models. By doing so, MCCLK effectively integrates item affinities into the recommendation process.
4. Empirical Validation: Extensive experiments on three benchmark datasets demonstrate MCCLK's superiority over state-of-the-art methods. The results highlight improvements in metrics such as AUC and F1-score, underlining the method's capability to learn improved representations for recommendation.

Theoretical and Practical Implications

The MCCLK model proposes a robust theoretical framework for enhancing recommendation systems by integrating knowledge from various data dimensions—structural, collaborative, and semantic. Practically, the proposed model could revolutionize how recommendation systems interact with multi-faceted data inputs, making them more effective in scenarios with limited direct supervision. The incorporation of self-supervised learning strategies also aligns well with contemporary trends in machine learning, where data efficiency and model robustness are crucial.

The implications of this research are manifold:

• Improved Representation Learning: By enhancing how user-item interactions and item properties are represented, MCCLK can potentially lead to more accurate and personalized recommendations.
• Efficiency in Sparse Data Environments: The method's ability to learn from limited labeled data makes it suitable for domains where user interactions are sparse.
• Future of Knowledge-aware Recommendations: As knowledge graphs continue to expand in various domains, the proposed cross-view learning method could be pivotal in designing future recommender systems that offer nuanced insights and recommendations.

Prospects for Future Research

Future research may explore the following directions based on this work:

• Extended Graph Views: Exploring additional graph views or variations could further enhance the richness of information used in the recommendations.
• Real-time Applications: Assessing the model's applicability and efficiency in real-time recommendation environments could provide insights into its practical deployment.
• Cross-domain Transferability: Investigating the model’s adaptability and performance across different recommendation domains might reveal insights into its versatility and robustness.

In conclusion, the MCCLK model represents a strategic advancement in the field of knowledge-aware recommendation, wherein leveraging the synergistic potential of contrastive learning with multi-view data yields significantly improved recommendation capabilities.