What question did this study set out to answer?

This research aims to systematically examine hybrid recommender systems combining collaborative filtering with knowledge-based techniques.

April 5, 2026Open Access

Hybridizing Collaborative Filtering and Knowledge: How do they Work Together? A Scoping Review

Key Points

This research aims to systematically examine hybrid recommender systems combining collaborative filtering with knowledge-based techniques.
Conducted a scoping review following PRISMA-ScR guidelines.
Analyzed 62 hybrid recommenders across various application domains.
Categorized hybridization strategies into model fusion, transfer learning, and hierarchical models.
Most hybrid approaches leverage graph-based models, deep learning architectures, and causal inference techniques.
Identified key challenges include computational complexity, lack of explainability, and bias in recommendations.
Highlighted scalability issues in knowledge graph maintenance and need for user-centered evaluation frameworks.

Abstract

The rapid expansion of digital platforms and the increasing complexity of user preferences have driven the need for more sophisticated recommendation systems. While Collaborative Filtering and Knowledge-Based Filtering have been widely adopted as core techniques for personalized recommendations, their individual limitations have led to the rise of hybrid approaches. Despite significant advancements, a comprehensive understanding of hybridization methodologies, their technical implementations, and emerging challenges remains unsolved. The purpose of this research is to systematically examine and synthe-size the domain of Hybrid Recommender Systems to address this. This study presents a scoping review, following the PRISMA-ScR guidelines, to systematically examine the domain of hybridizing Collaborative Filtering and Knowledge-Based Filtering. A total of 62 hybrid recommenders across various application domains were analyzed, and categorized into three primary hybridization strategies: Model Fusion, Transfer Learning, and Hierarchical Models. The review explores technical characteristics, hybridization techniques, data sources, evaluation methodologies, and domain-specific applications. Key findings indicate that most hybrid approaches focus on leveraging graph-based models, deep learning architectures, and causal inference techniques to enhance recommendation outcomes. However, despite these advancements, critical gaps remain. The review identifies key challenges, including computational complexity, lack of explainability, bias in recommendations, and reliance on offline evaluation metrics. Additionally, scalability issues in knowledge graph maintenance and the need for user-centered evaluation frameworks highlight important directions for future research. Addressing these gaps will be crucial in making hybrid recommendation systems more efficient, interpretable, and adaptable across diverse domains. This study contributes to the field by providing a structured synthesis of existing hybridization techniques, pinpointing success factors, and proposing future research avenues to advance hybrid recommendation systems.

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