KnowEx-HRS: An Adaptive Hybrid Recommendation System for Personalized Knowledge Discovery

Abstract

The rapid expansion of digital knowledge platforms has increased the need for intelligent recommendation systems capable of delivering personalized and context-aware suggestions. Traditional recommendation techniques such as Content-Based Filtering (CBF) and Collaborative Filtering (CF) exhibit complementary strengths but suffer from limitations including cold-start problems, overspecialization, and data sparsity when deployed independently. This paper proposes KnowEx-HRS, an adaptive hybrid recommendation framework that integrates CBF and CF through a dynamic weighting mechanism based on user interaction density. The content-based module leverages TFIDF feature extraction and cosine similarity, while the collaborative module utilizes user–item interaction matrices with neighbourhood-based prediction. An adaptive fusion layer balances both approaches to enhance robustness and scalability. The proposed system was evaluated on the MovieLens 100K dataset and a custom dataset containing 500 users and 1,000 items using Precision@K, Recall@K, and RMSE metrics. Experimental results demonstrate that KnowEx-HRS achieves a Precision@10 of 0.85 and Recall@10 of 0.79, outperforming standalone CBF and CF models by up to 12% in accuracy and significantly reducing prediction error. The findings validate the effectiveness of adaptive hybridization for robust personalized knowledge discovery.

Introduction

The text discusses the design, implementation, and evaluation of KnowEx-HRS, an adaptive hybrid recommendation system that integrates Content-Based Filtering (CBF) and Collaborative Filtering (CF) to provide accurate, scalable, and personalized recommendations in digital platforms.

Key Points:

1. Problem Context:
   • Users face an overwhelming amount of digital content, making recommendation systems essential for personalized discovery.
   • Traditional approaches:
     • CBF: Matches user profiles with item attributes for individual personalization but suffers from overspecialization and limited diversity.
     • CF: Leverages collective user behavior to enhance discovery but struggles with cold-start and sparse data problems.
2. Proposed Solution – KnowEx-HRS:
   • Combines CBF and CF using a dynamic hybridization mechanism that adjusts the relative weight of each technique based on user interaction density.
   • Ensures:
     • Strong personalization for new users (CBF emphasis)
     • Exploitation of community behavior for active users (CF emphasis)
   • Architecture:
     • User Interface (UI): Interactive dashboards for browsing, rating, and updating preferences.
     • Backend Services: FastAPI handles session management, feature extraction, and engine communication.
     • Database & Cache: PostgreSQL stores data; Redis ensures real-time responsiveness.
     • Recommendation Engine: Parallel CBF and CF modules fused adaptively.
3. System Modules:
   • CBF Module: Builds user profiles from TF-IDF weighted item metadata, computes similarity using cosine similarity, providing strong personalization.
   • CF Module: Uses user-item interaction matrices and matrix factorization to capture community preferences.

   • Adaptive Fusion: Final recommendation score is computed dynamically:

     SHybrid(u,i)=β(u)⋅SCBF(u,i)+(1−β(u))⋅SCF(u,i)S_{\text{Hybrid}}(u,i) = \beta(u) \cdot S_{\text{CBF}}(u,i) + (1-\beta(u)) \cdot S_{\text{CF}}(u,i)SHybrid?(u,i)=β(u)⋅SCBF?(u,i)+(1−β(u))⋅SCF?(u,i)

     Adjusts β based on user activity, mitigating cold-start issues and maintaining diversity.

4. Experimental Evaluation:
   • Datasets: MovieLens 100K and a custom dataset (500 users × 1,000 items).
   • Metrics: Precision@K, Recall@K, RMSE.
   • Results:
     • Hybrid model outperforms standalone CBF and CF:
       • Precision@10: 0.85 (vs. 0.72 CBF, 0.78 CF)
       • Recall@10: 0.79 (vs. 0.65 CBF, 0.71 CF)
       • RMSE: 0.75 (vs. 0.94 CBF, 0.88 CF)
     • Cold-start analysis shows hybrid model maintains performance by emphasizing content similarity for users with sparse interaction

Conclusion

This paper presented KnowEx-HRS, an adaptive hybrid recommendation framework designed to enhance personalized knowledge discovery by integrating Content Based Filtering (CBF) and Collaborative Filtering (CF). The proposed system leverages semantic feature extraction through TF-IDF and cosine similarity for individual-level personalization, while incorporating neighborhood-based collaborative modeling and matrix factorization to capture collective behavioral patterns. A key contribution of this work is the introduction of a dynamic ?-weighting mechanism, which adaptively balances content-based and collaborative signals based on user interaction density. This adaptive fusion strategy effectively mitigates cold-start and sparsity challenges while maintaining high recommendation accuracy and diversity. Experimental evaluation conducted on the MovieLens 100K dataset and a custom dataset demonstrated that the proposed hybrid model outperforms standalone CBF and CF approaches across multiple metrics. Specifically, KnowEx-HRS achieved superior Precision@10 and Recall@10 while significantly reducing RMSE, validating the robustness and predictive stability of the hybrid approach. The results confirm that adaptive hybridization provides a scalable and efficient framework for modern knowledge-driven platforms, where both personalization and community intelligence are essential. Future work will focus on incorporating deep learning-based embeddings, context-aware modeling, and reinforcement learning mechanisms to further enhance recommendation adaptability and explainability in real-time environments.

References

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Copyright

Copyright © 2026 Brijesh Gautam, Suraj Maurya, Mithilesh Vichare, Kirtida Naik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.