Towards a Reconciliation of Point-of-Interest Recommendation Systems Using Multi-Agent Systems

Authors

  • Zoulikha Maghni Sandid EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Algeria and Abdelhamid Ben Badis University of Mostaganem, Algeria https://orcid.org/0009-0003-0023-5918
  • Zohra SLAMA EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Algeria https://orcid.org/0000-0003-0528-8106
  • Nassim Dennouni EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Algeria and Higher School of Management, Tlemcen, Algeria https://orcid.org/0009-0006-8222-0796

DOI:

https://doi.org/10.13052/jmm1550-4646.2243

Keywords:

Recommendation systems, collaborative filtering, point-of-interest, cold-start problem, multi-agent system, reconciliation, Borda

Abstract

Point-of-interest (POI) recommendation is a good way to help Smartphone users discover new places by exploiting their preferences and social networks. However, this technique, which is often based on collaborative filtering, suffers from cold-start problem due to insufficient interaction with POIs (ratings) and the absence of declared friendship or trust relationships between users. To address this problem, one solution is to launch several Recommendations Systems (RS) simultaneously, using all available sources of information within the social network, especially in the case of a new user or a new POI. In this paper, a Multi-Agent System for Reconciling POI Recommendation Algorithms (MSRPRA) is proposed to exploit (1) the power of Pearson similarity deduced from POI ratings through the RatAg agent, (2) the effectiveness of Jaccard similarity derived from existing friendship relationships exploited by the FrAg agent and the contribution of trust scores declared by users using the TrAg agent. The system then uses a coordinator agent to merge, using the Borda voting method, the POIs lists generated by the RatAg, FrAg, and TrAg agents. The experimental results show that MSRPRA outperforms approaches based solely on Pearson similarity, Jaccard similarity, or explicit trust. On average, the model improves Precision by 7.38% and Recall by 7.73%, while achieving better precision in terms of RMSE, these results confirm the effectiveness of multi-agent fusion using Borda voting in improving the relevance of recommendations and mitigating the cold-start problem.

Downloads

Download data is not yet available.

Author Biographies

Zoulikha Maghni Sandid, EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Algeria and Abdelhamid Ben Badis University of Mostaganem, Algeria

Zoulikha Maghni Sandid is a PhD student in the computer science department at Sidi Bel-Abbes University, Algeria. She graduated in 2007. She received her post graduation degree in computer science at the University of Science and Technology Mohamed Boudiaf USTO-MB Oran, in 2011. Her research interests include ubiquitous computing, POI recommendation, orchestration of activities and Artificial Intelligence. She is a member of the ISIBA team of EEDIS laboratory at the computer science Department of Djillali Liabes University of Sidi Bel Abbes.

Zohra SLAMA, EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Algeria

Zohra Slama is an associate professor in computer science and ISIBA team leader of the EEDIS laboratory at the Djilali Liabes University of Sidi Bel Abbes. She received her PhD from Djillali Liabes University of Sidi Bel Abbes. Her major research area is paradigms of Artificial Intelligence, mobile learning, orchestration of activities, POI recommendation, mobile computing, and advanced databases.

Nassim Dennouni, EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Algeria and Higher School of Management, Tlemcen, Algeria

Nassim Dennouni is an associate professor at the Higher School of Management at Tlemcen, Algeria, since October 2022. He is ICAR team leader of the LIA Laboratory of the Hassiba Benbouali University of Chlef and he is also a member of the ISIBA team of EEDIS laboratory of Computer Science Department of Djillali Liabes University of Sidi Bel Abbes. He obtained his PhD and university accreditation from Djillali Liabes University in2016 and 2018, respectively. His primary research domains are Artificial Intelligence, ubiquitous computing, mobile learning, orchestration of activities, and POI recommendation.

References

Y. Yu, X. Chen, “A survey of point-of-interest recommendation in location-based social networks,” In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, April 2015.

J. D. Kelleher, Deep Learning. MIT Press, 2019.

K. Thakaeng, P. Temdee, S. Wicha, “A decision tree-based advisory recommendation system for dental students,” Journal of Mobile Multimedia, 21(01): 179–196, 2025. https://doi.org/10.13052/jmm1550-4646.2117.

D. Gavalas, C. Konstantopoulos, K. Mastakas, G. Pantziou, “Mobile recommender systems in tourism,” Journal of Network and Computer Applications, 39: 319–333, 2014.

V. Jain, B. Malviya, S. Arya, “An overview of electronic commerce (e-Commerce),” Journal of Contemporary Issues in Business and Government, 27(3): 665–670, 2021.

F. Ricci, L. Rokach, B. Shapira, Recommender Systems Handbook. Springer, 2015. https://doi.org/10.1007/978-1-4899-7637-6.

Z. Abbasi-Moud, H. Vahdat-Nejad, J. Sadri, “Tourism recommendation system based on semantic clustering and sentiment analysis,” Expert Systems with Applications, 167: 114324, 2021.

J.B. Griesner, T. Abdessalem, H. Naacke, “Intégration des Influences Géographique et Temporelle pour la Recommandation de Points d’Intérêt,” In Extraction et Gestion des Connaissances, 153–158, 2016.

D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, Y. Rui. “Geomf: Joint geographical modeling and matrix factorization for point-of-interest recommendation,” KDD’14, 2014.

L. Cai, J. Xu, J. Liu, T. Pei, “Integrating spatial and temporal contexts into a factorization model for POI recommendation,” International Journal of Geographical Information Science, 32(3): 524–546, 2018. DOI:10.1080/13658816.2017.1400550.

Y. Liu, M. Yin, X. Zhou, “A collaborative filtering algorithm with intragroup divergence for POI group recommendation,” Applied Sciences, 11(12): 5416, 2021.

Z. Maghni Sandid, Z. Slama, N. Dennouni, “Toward mitigating tourists’ indifference in POI recommendation systems using MAS,” NCAIA’2023, 75, 2023.

Z. F. Lansdowne, B. S. Woodward, “Applying the Borda ranking method,” Air Force Journal of Logistics, 20(2): 27–29, 1996.

A. Selmi, Z. Brahmi, M. Gammoud, “Multi-agent recommender system: State of the art,” In Proceedings of the 16th International Conference on Information and Communications Security, 2014.

L. Sebastia, A. Giret, I. Garcia, “A multi agent architecture for tourism recommendation,” In Trends in Practical Applications of Agents and Multiagent Systems: 8th International Conference on Practical Applications of Agents and Multiagent Systems, 547–554, 2010.

P. Bedi, S. K. Agarwal, V. Jindal, Richa, “MARST: Multi-Agent Recommender System for e-Tourism Using Reputation Based Collaborative Filtering,” In Databases in Networked Information Systems: 9th International Workshop, DNIS 2014, Aizu-Wakamatsu, Japan, March 24–26, 2014.

A. J. Morais, E. Oliveira, A. M. Jorge, “A multi-agent recommender system,” In Distributed Computing and Artificial Intelligence: 9th International Conference,” 281–288, Nov. 2012. Springer Berlin Heidelberg. doi:10.1007/978-3-642-28765-7_33.

C. Cavallaro, G. Verga, E. Tramontana, O. Muscato, “Multi-agent architecture for point of interest detection and recommendation,” In Proceedings of the CEUR Workshop Proceedings, 2404, 98–104, 2019.

A. M. Mansour, M. A. Obeidat, B. Hawashin, “A novel multi agent recommender system for user interests extraction,” Cluster Computing, 26(2): 1353–1362, 2023. doi:10.1007/s10586-022-03655-7.

H. Kaur, H. Kaur, A. Singh, “Multi-agent based recommender system for Netflix,” In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India, 211–221, 2020.

M. Amane, K. Aissaoui, M. Berrada, “A multi-agent and content-based course recommender system for university e-learning platforms,” In International Conference on Digital Technologies and Applications, 663–672, Jan. 2021. Cham: Springer International Publishing. doi:10.1007/978-3-030-73882-2_60.

A. Alhejaili, S. Fatima, “Multi-agent recommender system,” In Recent Advances in Agent-based Negotiation: Formal Models and Human Aspects. Springer Singapore, 103–119, 2021. doi:10.1007/978-981-16-0471-3_7.

A. Dorri, S. S. Kanhere, R. Jurdak. “Multi-agent systems: A survey,” IEEE Access, 6: 28573–28593, 2018.

D. G. Pruitt, “Negotiation behavior,” Academic Press, 2013.

S. Kraus, “Negotiation and cooperation in multi-agent environments,” Artificial Intelligence, 94(1–2): 79–97, 1997.

A. R. Panisson, P. McBurney, R. H. Bordini, “A computational model of argumentation schemes for multi-agent systems,” Argument & Computation, 12(3): 357–395, 2021.

J. Pitt, L. Kamara, M. Sergot, A. Artikis, “Voting in multi-agent systems,” The Computer Journal, 49(2): 156–170, 2006.

H. Papadakis, A. Papagrigoriou, C. Panagiotakis, E. Kosmas, P. Fragopoulou, “Collaborative filtering recommender systems taxonomy,” Knowledge and Information Systems, 64(1): 35–74, 2022.

A. K. Hossain, Z. Tasnim, S. Hoque, M. A. Rahman, “A recommender system for adaptive examination preparation using Pearson correlation collaborative filtering,” International Journal of Automation, Artificial Intelligence and Machine Learning, 2(1): 30–43, 2021.

J. D. Zhang, C. Y. Chow, “Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations,” In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 443–452, 2015.

S. Bag, S. K. Kumar, M. K. Tiwari, “An efficient recommendation generation using relevant Jaccard similarity,” Information Sciences, 483: 53–64, 2019.

S. Medjroud, N. Dennouni, M. Loukam, “Towards a systematic point-of-interest recommendations based on trust between users deduced from their ratings and check-ins in a LBSN,” International Journal of Computing and Digital Systems, 16(1): 189–203, 2024.

P. Wang, Y. Fu, P. Liu, B. Zhu, F. Wang, D. Pamucar, “Evaluation of ecological governance in the Yellow River basin based on Uninorm combination weight and MULTIMOORA-Borda method,” Expert Systems with Applications, 235: 121–227, 2024.

M. Hadjhenni, N. Dennouni, Z. Slama, “Toward a systematic evaluation approach of point-of-interest recommendation algorithms of a novel smart tourism tool,” International Journal of Computing and Digital Systems, 15(1): 653–670, 2024.

H. J. C. Pampin, H. Jerbi, M. P. O’Mahony, “Evaluating the relative performance of collaborative filtering recommender systems,” Journal of Universal Computer Science, 21(13): 1849–1868, 2015.

P. Jing, “Digital media visual recommendation system based on artificial neural network machine learning,” Journal of Mobile Multimedia, 21(05): 967–996, 2025. https://doi.org/10.13052/jmm1550-4646.2156.

Downloads

Published

2026-09-19

How to Cite

Sandid, Z. M., SLAMA, Z., & Dennouni, N. . (2026). Towards a Reconciliation of Point-of-Interest Recommendation Systems Using Multi-Agent Systems. Journal of Mobile Multimedia, 22(4), 475–496. https://doi.org/10.13052/jmm1550-4646.2243

Issue

2026: Vol 22 Iss 4

Section

Articles