Knowledge Graph Contrastive Learning for Recommendation (2205.00976v2)

Abstract: Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities. Such KG sparsity and noise make the item-entity dependent relations deviate from reflecting their true characteristics, which significantly amplifies the noise effect and hinders the accurate representation of user's preference. To fill this research gap, we design a general Knowledge Graph Contrastive Learning framework (KGCL) that alleviates the information noise for knowledge graph-enhanced recommender systems. Specifically, we propose a knowledge graph augmentation schema to suppress KG noise in information aggregation, and derive more robust knowledge-aware representations for items. In addition, we exploit additional supervision signals from the KG augmentation process to guide a cross-view contrastive learning paradigm, giving a greater role to unbiased user-item interactions in gradient descent and further suppressing the noise. Extensive experiments on three public datasets demonstrate the consistent superiority of our KGCL over state-of-the-art techniques. KGCL also achieves strong performance in recommendation scenarios with sparse user-item interactions, long-tail and noisy KG entities. Our implementation codes are available at https://github.com/yuh-yang/KGCL-SIGIR22

An Examination of "Knowledge Graph Contrastive Learning for Recommendation"

The paper "Knowledge Graph Contrastive Learning for Recommendation" presents a novel framework titled KGCL (Knowledge Graph Contrastive Learning) designed to enhance recommender systems through the integration of contrastive learning paradigms and knowledge graph embeddings. This work provides a sophisticated approach to mitigating common issues such as KG sparsity and noise, which have traditionally hindered the performance and accuracy of recommendation systems utilizing knowledge graphs.

The authors thoughtfully identify and address significant challenges associated with knowledge graph-enhanced recommender systems. They recognize that traditional systems often falter due to the long-tail distribution of entities within knowledge graphs and the presence of noisily linked, topic-irrelevant entities. The sparsity of supervision signals and the noise introduced by irrelevant connections degrade the representation of items and hinder the system's ability to accurately reflect user preferences.

KGCL proposes to tackle these challenges by employing a contrastive learning framework specifically adjusted to the unique characteristics of knowledge graph environments. The framework applies a knowledge graph augmentation schema that suppresses noise and enhances the robustness of item representations. Furthermore, it establishes a cross-view contrastive learning paradigm to provide additional supervisory signals, thereby prioritizing unbiased user-item interactions.

Throughout their experimental evaluation, the authors showcase the consistent superiority of KGCL over various state-of-the-art recommendation techniques. Notably, KGCL performs well under scenarios characterized by sparse user-item interactions and noisy, long-tail KG entities. It is clear that the framework effectively utilizes the inherent semantic relationships in knowledge graphs to improve recommendation accuracy.

The implications of this research have both practical and theoretical dimensions. Practically, the methods proposed provide a robust tool for recommender systems facing the challenges of sparse and noisy data, common in real-world applications. Theoretically, the research expands the application of contrastive learning frameworks, demonstrating their adaptability and efficacy in domains beyond conventional areas such as computer vision or natural language processing.

Future research could explore the extension of the KGCL framework to additional domains where knowledge graphs provide critical semantic context. Also, further refinement of the contrastive learning approach, especially in terms of optimizing the balance between positive and negative pairs in contexts with extreme data sparsity, could yield even greater performance improvements.

The methodology outlined in the paper suggests a paradigm shift towards more semantically aware and noise-resilient recommender systems. This shift aligns well with contemporary trends in AI and machine learning, emphasizing interpretability and robustness of models in real-world conditions. The integration of knowledge graph representations with advanced learning paradigms such as contrastive learning marks a promising direction in the quest for enhancing the quality and reliability of automated recommender systems.