Improving Ranking Quality via Consequent Set Quartile Partitioning in Rule-Based Recommendation
Tubagus Mohammad Akhriza, Khoerul Anwar, Weda Adistianaya Dewa · DOAJ (DOAJ: Directory of Open Access Journals) · 2026
Accurate ranking remains challenging in session-based recommender systems, particularly in dynamic domains such as e-commerce and digital news. Neural models can achieve high accuracy but require substantial computational resources, while Association Rule (AR)-based methods are computationally efficient yet may rank relevant items poorly when candidate weights are similar. The research aims to improve the ranking quality of AR-based session recommendations while retaining computational efficiency through Association Rule with Top-k Quartile Filtering (ART-Q). The proposed framework constructs an association-rule dictionary, partitions each consequent set into four quartiles according to rule weights, and independently selects top-k candidates from each quartile. ART-Q is evaluated using two real-world datasets: YooChoose (YOO) and Malang Posco Media (MPM). Performance is evaluated using HR@20, MRR@20, and NDCG@20 against AR, k-Nearest Neighbor (KNN), and neural baselines. On YOO, ART-Q-V6 achieves HR@20 of 0.6899, MRR@20 of 0.7500, and NDCG@20 of 0.5436, representing gains of 11.9% in HR and 31.6% in Normalized Discounted Cumulative Gain (NDCG) over traditional AR, with more than twofold improvement in Mean Reciprocal Rank (MRR). On MPM, ART-Q-V1 achieves HR@20 of 0.7498, MRR@20 of 0.2717, and NDCG@20 of 0.3260, outperforming all evaluated baselines. ART-Q also completes training and inference in less than two minutes on a laptop CPU, demonstrating its computational efficiency. These results indicate that quartile-based filtering effectively reduces ranking ambiguity while maintaining a lightweight recommendation framework.