Improving Ranking Quality via Consequent Set Quartile Partitioning in Rule-Based Recommendation

Tubagus Mohammad Akhriza; Khoerul Anwar; Weda Adistianaya Dewa

doi:10.21512/commit.v20i2.13889

Authors

Tubagus Mohammad Akhriza STMIK PPKIA Pradnya Paramita
Khoerul Anwar STMIK PPKIA Pradnya Paramita
Weda Adistianaya Dewa STMIK PPKIA Pradnya Paramita

DOI:

https://doi.org/10.21512/commit.v20i2.13889

Keywords:

Association rules, Ranking Quality, Recommendation System, Session-Based Recommendation, Top-k Recommendation

Abstract

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.

Dimensions

Author Biographies

Tubagus Mohammad Akhriza, STMIK PPKIA Pradnya Paramita

Department of Information System

Khoerul Anwar, STMIK PPKIA Pradnya Paramita

Department of Information Technology

Weda Adistianaya Dewa, STMIK PPKIA Pradnya Paramita

Department of Information System

References

[1] S. Raza and C. Ding, “News recommender system: A review of recent progress, challenges, and opportunities,” Artificial Intelligence Review, vol. 55, no. 1, pp. 749–800, 2022.

[2] D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” Journal of Big Data, vol. 9, pp. 1–36, 2022.

[3] X. Huang, Y. He, B. Yan, and W. Zeng, “Fusing frequent sub-sequences in the session-based recommender system,” Expert Systems with Applications, vol. 206, 2022.

[4] M. Ludewig, N. Mauro, S. Latifi, and D. Jannach, “Empirical analysis of session-based recommendation algorithms,” User Modeling and User-Adapted Interaction, vol. 31, pp. 149–181, 2021.

[5] S. Wang, L. Cao, Y. Wang, Q. Z. Sheng, M. A. Orgun, and D. Lian, “A survey on session-based recommender systems,” ACM Computing Surveys (CSUR), vol. 54, no. 7, pp. 1–38, 2021.

[6] K. Wu and K. Chi, “Enhanced e-commerce customer engagement: A comprehensive three-tiered recommendation system,” Journal of Knowledge Learning and Science Technology, vol. 2, no. 3, pp. 348–359, 2023.

[7] Z. Zhu et al., “Understanding or manipulation: Rethinking online performance gains of modern recommender systems,” ACM Transactions on Information Systems, vol. 42, no. 4, pp. 1–32, 2024.

[8] B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based recommendations with recurrent neural networks,” 2016. [Online]. Available: https://arxiv.org/abs/1511.06939

[9] F. Yuan, A. Karatzoglou, I. Arapakis, J. M. Jose, and X. He, “A simple convolutional generative network for next item recommendation,” in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. Melbourne, VIC, Australia: Association for Computing Machinery, Feb. 11–15 2019, pp. 582–590.

[10] S. Wu et al., “Session-based recommendation with graph neural networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 346–353, 2019.

[11] Z. Wang, “Empowering few-shot recommender systems with large language models-enhanced representations,” IEEE Access, vol. 12, pp. 29 144–29 153, 2024.

[12] W. Li, Y. Cai, M. H. Hanafiah, and Z. Liao, “An empirical study on personalized product recommendation based on cross-border e-commerce customer data analysis,” Journal of Organizational and End User Computing (JOEUC), vol. 36, no. 1, pp. 1–16, 2024.

[13] S. Z. U. Hassan, M. Rafi, and J. Frnda, “GCZRec: Generative collaborative zero-shot framework for cold start news recommendation,” IEEE Access, vol. 12, pp. 16 610–16 620, 2024.

[14] T. N. Dinh, B. D. Nguyen, X. D. Vu, and Q. D. Truong, “Improve medicine prescribing performance using recommendation systems,” in International Conference on Intelligent Systems and Data Science. Can Tho, Vietnam: Springer, Nov. 11–12, 2023, pp. 304–312.

[15] M. Ludewig and D. Jannach, “Evaluation of session-based recommendation algorithms,” User Modeling and User-Adapted Interaction, vol. 28, pp. 331–390, 2018.

[16] H. S. Sheu and S. Li, “Context-aware graph embedding for session-based news recommendation,” in Proceedings of the 14th ACM Conference on Recommender Systems. Virtual: Association for Computing Machinery, Sep. 22–26, 2020, pp. 657–662.

[17] R. Yeganegi, S. Haratizadeh, and M. Ebrahimi, “STAR: A session-based time-aware recommender system,” Neurocomputing, vol. 573, 2024.

[18] T. M. Akhriza and I. D. Mumpuni, “A timewindow approach to recommending emerging and on-the-rise items,” in 2022 Seventh International Conference on Informatics and Computing (ICIC). Bali, Indonesia: IEEE, Dec. 8–9, 2022, pp. 1–8.

[19] F. Antonello, P. Baraldi, E. Zio, and L. Serio, “A novel metric to evaluate the association rules for identification of functional dependencies in complex technical infrastructures,” Environment Systems and Decisions, vol. 42, pp. 436–449, 2022.

[20] J. H. Chowdhury, M. B. Hossain, M. S. Kaiser, and M. S. Arefin, “Performance comparisons in association rule mining over public datasets,” in Proceedings of the International Conference on Big Data, IoT, and Machine Learning: BIM 2021. Cox’s Bazar, Bangladesh: Springer, 2022, pp. 761–775.

[21] D. Liu, “Design of data mining system for sports training biochemical indicators based on artificial intelligence and association rules,” International Journal of Data Mining and Bioinformatics, vol. 28, no. 3-4, pp. 236–256, 2024.

[22] L. Wang, L. Gui, and P. Xu, “Incremental sequential patterns for multivariate temporal association rules mining,” Expert Systems with Applications, vol. 207, 2022.

[23] J. Li and S. Bao, “Personalized recommendation method of e-commerce products based on indepth user interest portraits,” International Journal of Information Technology and Web Engineering (IJITWE), vol. 19, no. 1, pp. 1–15, 2024.

[24] Z. Chen, W. Gan, J. Wu, K. Hu, and H. Lin, “Data scarcity in recommendation systems: A survey,” ACM Transactions on Recommender Systems, vol. 3, no. 3, pp. 1–31, 2025.

[25] N. Liu and J. Zhao, “Recommendation system based on deep sentiment analysis and matrix factorization,” IEEE Access, vol. 11, pp. 16 994–17 001, 2023.

[26] S. Kanwal, S. Nawaz, M. K. Malik, and Z. Nawaz, “A review of text-based recommendation systems,” IEEE Access, vol. 9, pp. 31 638–31 661, 2021.

[27] J. Polohakul, E. Chuangsuwanich, A. Suchato, and P. Punyabukkana, “Real estate recommendation approach for solving the item cold-start problem,” IEEE Access, vol. 9, pp. 68 139–68 150, 2021.

[28] A. Gharahighehi and C. Vens, “Diversification in session-based news recommender systems,” Personal and Ubiquitous Computing, vol. 27, pp. 5–15, 2023.

[29] G. De Souza Pereira Moreira, S. Rabhi, J. M. Lee, R. Ak, and E. Oldridge, “Transformers4rec: Bridging the gap between NLP and sequential/session-based recommendation,” in Proceedings of the 15th ACM conference on recommender systems. Amsterdam, Netherlands: Association for Computing Machinery, Sep. 27–Oct. 1, 2021, pp. 143–153.

[30] P. Symeonidis, L. Kirjackaja, and M. Zanker, “Session-based news recommendations using SimRank on multi-modal graphs,” Expert Systems with Applications, vol. 180, 2021.

[31] M. Karimi, B. Cule, and B. Goethals, “Leveraging sequential episode mining for session-based news recommendation,” in International Conference on Web Information Systems Engineering. Melbourne, VIC, Australia: Springer, Oct. 25–27, 2023, pp. 594–608.

[32] A. Smets, J. Hendrickx, and P. Ballon, “We’re in this together: A multi-stakeholder approach for news recommenders,” Digital Journalism, vol. 10, no. 10, pp. 1813–1831, 2022.

[33] F. Yuan, “Generative model for sequential recommendation based on Convolution Neural Networks (CNN),” 2024. [Online]. Available: https: //github.com/fajieyuan/WSDM2019-nextitnet

[34] Malte, N. Mauro, S. Latifi, and T. A. Motta, “Python-based framework for building and evaluating session-based and sessionaware recommender systems,” 2021. [Online]. Available: https://github.com/rn5l/session-rec

[35] S. Latifi, N. Mauro, and D. Jannach, “Sessionaware recommendation: A surprising quest for the state-of-the-art,” Information Sciences, vol. 573, pp. 291–315, 2021.

[36] Y. Ouyang, H. Long, R. Gao, and J. Liu, “Course recommendation model based on layer dropout graph differential contrastive learning,” IEEE Access, vol. 12, pp. 7762–7774, 2024.

[37] L. H. Baniata, S. Kang, M. A. Alsharaiah, and M. H. Baniata, “Advanced deep learning model for predicting the academic performances of students in educational institutions,” Applied Sciences, vol. 14, no. 5, pp. 1–19, 2024.

[38] M. Ferrari Dacrema, S. Boglio, P. Cremonesi, and D. Jannach, “A troubling analysis of reproducibility and progress in recommender systems research,” ACM Transactions on Information Systems (TOIS), vol. 39, no. 2, pp. 1–49, 2021.

[39] R. M. Fajri, A. Saxena, Y. Pei, and M. Pechenizkiy, “FAL-CUR: Fair active learning using uncertainty and representativeness on fair clustering,” Expert Systems with Applications, vol. 242, pp. 1–12, 2024.

[40] Y. Koguma, “Tabu search-based heuristic solver for general integer linear programming problems,” IEEE Access, vol. 12, pp. 19 059–19 076, 2024.

[41] J. Ahamed, M. N. Noori, and M. Ahmed, “Matrix factorization and cosine similarity based recommendation system for cold start problem in ecommerce industries,” International Journal of Computing and Digital Systems, vol. 15, no. 1, pp. 775–787, 2024.