Design an Intelligent Multi-agent Computer-aided System for Recommender Systems

Subject Areas : Computer Engineering

Ramazan Teimouri Yansari ¹ , Mojtaba Ajoudani ² , Seyed Reza Mosayyebi ³

1 - Department of Computer Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran
2 - Department of Electrical Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran
3 - Department of Electrical Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran

Received: 2023-08-11 Accepted : 2023-11-13 Published : 2023-12-01

Keywords: Machine Learning, Recommender Systems, Intelligent agents, Computer-aided System, Multi-agent systems (MAS),

Abstract :

Abstract – Due to the increasing amount of information and services available on the web, it is necessary to provide tools such as recommender systems to websites and applications that can help users find information and services that suit their interests. For this reason, providing appropriate guidance and suggestions to users in different choices, according to the user's priorities, has found a special position in different fields. Recommender systems are information systems that help in the decision-making process by modeling the behavior of users in operational environments in ranking, comparing, selecting and preferring user items, by limiting the search space through high-quality and accurate recommendations. In this research, a multi-agent recommender system was proposed that can provide suitable recommendations as a shopping assistant in the purchasing process. To analyze the proposed model, the sales dataset of an online store including 1067371 records of online sales data has been used. According to the results, in this evaluation, the accuracy of the proposed model was 91.5% on average. By combining multi-agent systems, multi-agent recommender systems were proposed that can provide suitable recommendations as a purchasing assistant in the purchasing process. The results of applying the proposed model on the data related to the purchase history of the customers of an online shopping showed that the proposed model has a good efficiency in evaluating the parameters used in comparison with the common methods in this property field.

References:

[1]           L. Sebastia, A. Giret, and 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, 2010: Springer, pp. 547-554.

[2]           P. G. Balaji and D. Srinivasan, "An Introduction to Multi-Agent Systems," in Innovations in Multi-Agent Systems and Applications - 1, D. Srinivasan and L. C. Jain Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 1-27.

[3]           A. Dorri, S. S. Kanhere, and R. Jurdak, "Multi-Agent Systems: A Survey," IEEE Access, vol. 6, pp. 28573-28593, 2018, doi: 10.1109/ACCESS.2018.2831228.

[4]           L. O. Colombo-Mendoza, R. Valencia-García, A. Rodríguez-González, G. Alor-Hernández, and J. J. Samper-Zapater, "RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes," Expert Systems with Applications, vol. 42, no. 3, pp. 1202-1222, 2015/02/15/ 2015, doi: https://doi.org/10.1016/j.eswa.2014.09.016.

[5]           M. Altulyan, L. Yao, X. Wang, C. Huang, S. S. Kanhere, and Q. Z. Sheng, "A survey on recommender systems for Internet of Things: techniques, applications and future directions," The Computer Journal, vol. 65, no. 8, pp. 2098-2132, 2022.

[6]          D. Roy and M. Dutta, "A systematic review and research perspective on recommender systems," Journal of Big Data, vol. 9, no. 1, p. 59, 2022/05/03 2022, doi: 10.1186/s40537-022-00592-5.

[7]           E. Kozegar, H. Yarmohammadi, M. Bazargani, and Z. Homayounpour, "Presenting a novel method based on collaborative filtering for nearest neighbor detection in recommender systems," Intelligent Multimedia Processing and Communication Systems (IMPCS), vol. 1, no. 1, pp. 55-64, 2020. [Online]. Available: https://impcs.zanjan.iau.ir/article_683460_25794b39a6dcb9052a73c2c1fb5ed680.pdf.

[8]           J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, "Collaborative filtering recommender systems," The adaptive web: methods and strategies of web personalization, pp. 291-324, 2007.

[9]           A. Oroojlooy and D. Hajinezhad, "A review of cooperative multi-agent deep reinforcement learning," Appl Intell, vol. 53, no. 11, pp. 13677-13722, 2023/06/01 2023, doi: 10.1007/s10489-022-04105-y.

[10]         K. Zhang, Z. Yang, and T. Başar, "Multi-agent reinforcement learning: A selective overview of theories and algorithms," Handbook of reinforcement learning and control, pp. 321-384, 2021.

[11]         R. Teimouri Yansari and M. Ajoudani, "Development of a multi-agent recommender system for intelligent shopping assistants " Intelligent Multimedia Processing and Communication Systems (IMPCS), vol. 4, no. 2, pp. 1-10, 2023.

[12]         T. Pinto, R. Faia, M. Navarro-Caceres, G. Santos, J. M. Corchado, and Z. Vale, "Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings," IEEE Systems Journal, vol. 13, no. 1, pp. 1084-1095, 2019, doi: 10.1109/JSYST.2018.2876933.

[13]         J. Neto, A. J. Morais, R. Gonçalves, and A. L. Coelho, "Multi-Agent-Based Recommender Systems: A Literature Review," Singapore, 2022: Springer Singapore, in Proceedings of Sixth International Congress on Information and Communication Technology, pp. 543-555.

[14]         U. a. W. Pakdeetrakulwong, Pornpit, "State of the art of a multi-agent based recommender system for active software engineering ontology," State of the art of a multi-agent based recommender system for active software engineering ontology, vol. 3, no. 4, pp. 29-42, 2013.

[15]         H. Pandey and V. Singh, "A fuzzy logic based recommender system for e-learning system with multi-agent framework," International Journal of Computer Applications, vol. 122, no. 17, 2015.

[16]         M. Keykhaee, M. JelokhaniNiaraki, and N. Mahmoody Vanolya, "Design and Development of a Tourism Recommender System using Volunteered Geographic Information, Case Study: Yazd," urban tourism, vol. 8, no. 3, pp. 137-149, 2021, doi: 10.22059/jut.2021.323345.902.

[17]         R. Teimouri Yansari, "Designing a multi-agent system for intelligent shopping assistant," presented at the The first national conference on computers, information technology and Islamic communications in Iran, 2015.

[18]         V. Mohammadi, M. Hosseinzadeh, and M. H. Hosseinzadeh, "Schematic Design of Hotel Recommendation Systems by user Precedence on Twitter," Business Intelligence Management Studies, vol. 7, no. 25, pp. 85-118, 2018, doi: 10.22054/ims.2018.9745.

[19]         J. Neto, A. J. Morais, R. Gonçalves, and A. L. Coelho, "Context-Based Multi-Agent Recommender System, Supported on IoT, for Guiding the Occupants of a Building in Case of a Fire," Electronics, vol. 11, no. 21, p. 3466, 2022. [Online]. Available: https://www.mdpi.com/2079-9292/11/21/3466.

[20]         A. Forestiero, "Multi-Agent Recommendation System in Internet of Things," in 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 14-17 May 2017 2017, pp. 772-775, doi: 10.1109/CCGRID.2017.123.

[21]         V. N. Marivate, G. Ssali, and T. Marwala, "An intelligent Multi-Agent recommender system for human capacity building," in MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference, 5-7 May 2008 2008, pp. 909-915, doi: 10.1109/MELCON.2008.4618553.

[22]         R. Taimourei-Yansary, M. Mirzarezaee, M. Sadeghi, and B. Nadjar Araabi, "Predicting Invasive Disease-Free Survival Time in Breast Cancer Patients Using Graph-based Semi-Supervised Machine Learning Techniques," (in en), Soft Computing Journal, vol. 10, no. 1, pp. 48-69, 2022, doi: 10.22052/scj.2022.243330.1039.

[23]         R. Teimouri Yansari, M. Mirzarezaee, M. Sadeghi, and B. Nadjar Araabi, "A New Survival Analysis Model in Adjuvant Tamoxifen-Treated Breast Cancer Patients Using Manifold-based Semi-Supervised Learning," Journal of Computational Science, p. 101645, 2022, doi: https://doi.org/10.1016/j.jocs.2022.101645.

[24]         A. Tharwat, "Classification assessment methods," Applied Computing and Informatics, vol. 17, no. 1, pp. 168-192, 2020, doi: https://doi.org/10.1016/j.aci.2018.08.003.

[25]         E. Aghaenjad, R. Taimourei-Yansary, and A. Riahi, "A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds," (in eng), Journal of Health and Biomedical Informatics, Original Article vol. 6, no. 2, pp. 101-110, 2019. [Online]. Available: http://jhbmi.ir/article-1-278-en.html.