Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.32464

Adaptive Personalization for Tourism Recommendations: A Hybrid CBR-AHP-TOPSIS Framework with Context-Aware Mode Adjustment

Edi Faizal ORCID..., Thomas Edyson Tarigan ORCID..., Sumiyatun Sumiyatun ORCID...
Faculty of Information Technology, Indonesia Digital Technology University, Yogyakarta, Indonesia

Background: Conventional case-based reasoning (CBR) recommender systems employ fixed similarity weights, failing to capture dynamic user preferences across travel contexts.

Objective: This paper proposes an adaptive hybrid framework integrating CBR with multi-criteria decision making (AHP and TOPSIS) for context-aware personalization in tourism recommendations.

Methods: Weights of criteria are derived from user surveys using the analytic hierarchy process (AHP) and fine-tuned using transaction-informed mode-specific adjustment factors calculated from historical rating patterns. The framework is evaluated on a real-world tourism dataset from Yogyakarta, Indonesia (15,870 records).

Results: The proposed method achieves a precision of 0.85, recall of 0.88, F1-score of 0.86 and RMSE of 0.27. Compared to feature-enhanced deep learning baselines (LightGCN-feature, SASRec-feature), it achieves 4.9% higher precision and 3.6% higher F1-score. Statistical tests (Friedman, Wilcoxon) confirm medium-to-large effect sizes (r = 0.63–0.92). Cross-regional validation (Bali, Malang), cold-start evaluation and bootstrap confidence intervals support the robustness of the framework within the tested regional subsets, particularly in sparse-data scenarios.

Conclusion: The proposed framework offers an interpretable, generalizable and efficient approach to context-aware personalization, with documented practical implications for destination management organizations.

Keywords: Recommender systems; Case-based reasoning; Multi-criteria decision making; Analytic hierarchy process; TOPSIS; Context-aware personalization; Tourism informatics.

Received: April 26, 2026; Revised: July 17, 2026; Accepted: July 21, 2026; Prepublished online: November 8, 2026 

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