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Information Systems Frontiers
Erschienen in:

11.01.2023

Supervised Machine Learning for Matchmaking in Digital Business Ecosystems and Platforms

verfasst von: Mustapha Kamal Benramdane, Elena Kornyshova, Samia Bouzefrane, Hubert Maupas

Erschienen in: Information Systems Frontiers | Ausgabe 4/2024

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Abstract

In the digital era, organizations belonging to the same or different market segments come together in digital platforms that allow them to exchange. These organizations are unified within a Digital Business Ecosystem. However, the rapid growth of the number of these organizations accentuates the complexity of finding economic partners, customers, suppliers, or other organizations that can share economic interests. In our research, we propose a recommendation system that is implemented on such a digital platform, and which is based on matchmaking and hybrid supervised machine learning algorithms. In this paper, we provide a detailed analysis of the functioning of this system, the challenge encountered when processing the data which made it possible to highlight the similarities between the organizations that can be associated. Thus, we seek to improve the understanding and analysis of the data for the identification of partners in an optimal way.
Vorheriger Artikel Comparison of Semantic Similarity Models on Constrained Scenarios
Nächster Artikel Transfer Learning Enabled Bearing Fault Detection Methods Based on Image Representations of Single-Dimensional Signals

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Literatur
Bao, J., & Xia, J. (2017). A hybrid algorithm for service matchmaking based on ontology approach. In 2017 IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC) (pp. 2420-2424). IEEE.
Boroń, M., Brzeziński, J., & Kobusińska, A. (2020). P2P Matchmaking solution for online games. Peer-To-Peer Networking and Applications, 13(1), 137–150.CrossRef
Cassar, G., Barnaghi, P., & Moessner, K. (2013). Probabilistic matchmaking methods for automated service discovery. IEEE Transactions on Services Computing, 7(4), 654–666.CrossRef
Celik, D., & Elci, A. (2005). Searching semantic Web services: An intelligent agent approach using semantic enhancement of client input term (s) and matchmaking step. In International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06) (vol. 2, pp. 916-922). IEEE.
Chen, Y., Li, X., Liu, J., & Ying, Z. (2018). Recommendation system for adaptive learning. Applied Psychological Measurement, 42(1), 24–41.CrossRef
Christiansen, A.H., Nielsen, B.F., & Gensby, E. (2018). Multi-parameterised matchmaking: a framework. In 2018 IEEE Conference on computational intelligence and games (CIG) (pp. 1–4). IEEE.
Cisneros-Cabrera, S., Pishchulov, G., Sampaio, P., Mehandjiev, N., Liu, Z., & Kununka, S. (2021). An approach and decision support tool for forming industry 4.0 supply chain collaborations. Computers in Industry, 125, 103391.CrossRef
Decker, K., Williamson, M., & Sycara, K. (1996). Matchmaking and brokering. In Proceedings of the second international conference on multi-agent systems (ICMAS-96) (vol. 432, No. December).
ESSAYAH, A., Youssfi, M., Bouattane, O., & Illoussamen, E. (2019). QoS-based semantic micro services discovery and composition using ACO Algorithm.
Evans, D.S. (2016). Matchmakers: the new economics of multisided platforms. Harvard Business Review Press.
Friedrich, H., Kleedorfer, F., Human, S., & Huemer, C. (2016). Integrating matching services into the web of needs. In SEMANTiCS (Posters, Demos, SuCCESS).
Gonzalez-Castillo, J., Trastour, D., & Bartolini, C. (2001). Description logics for matchmaking of services. HP LABORATORIES TECHNICAL REPORT HPL, (265).
Henderson, R., & Tweten, D. (1996). Portable batch system: external reference specification. Technical report, NASA, Ames Research Center.
Iansiti, M., & Levien, R. (2004). Strategy as ecology. Harvard business review, 82(3), 68–78.
Imran, K., Ullah, K., Khattak, A., Zhang, J., Pal, A., Rafique, M.N., & Baig, S.M. (2020). Matchmaking model for bilateral trading decisions of load serving entity. Electric Power Systems Research, 183, 106281.CrossRef
Ipsen, N.B., Mattei, P.A., & Frellsen, J. (2020). not-MIWAE: Deep generative modelling with missing not at random data. arXiv:2006.​12871.
Ivanov, O., & Figurnov, M. (2018). Variational autoencoder with arbitrary conditioning. arXiv:1806.​02382.
Jadhav, A., Pramod, D., & Ramanathan, K. (2019). Comparison of performance of data imputation methods for numeric dataset. Applied Artificial Intelligence, 33(10), 913–933.CrossRef
Kazantsev, N., Pishchulov, G., Mehandjiev, N., Sampaio, P., & Zolkiewski, J. (2022). Investigating barriers to demand-driven SME collaboration in low-volume high-variability manufacturing. Supply Chain Management: An International Journal, 27(2), 265–282. https://​doi.​org/​10.​1108/​SCM-10-2021-0486.CrossRef
Khan, S.I., & Hoque, A.S.M.L. (2020). SICE: an improved missing data imputation technique. Journal of Big Data, 7(1), 1–21.CrossRef
Klochkov, Y, & Zhivotovskiy, N. (2021). Stability and deviation optimal risk bounds with convergence rate O(1/n). arXiv:2103.​12024.
Koutsikouri, D., Lindgren, R., Henfridsson, O., & Rudmark, D. (2018). Extending digital infrastructures: a typology of growth tactics. Journal of the Association for Information Systems, 19(10), 2.
Lagraa, S., Seba, H., & Kheddouci, H. (2011). Matchmaking OWL-s processes: an approach based on path signatures. In Proceedings of the international conference on management of emergent digital EcoSystems, pp. 169–176.
Ludwig, S.A., & Schoene, T. (2012). Matchmaking in multi-attribute auctions using a genetic algorithm and a particle swarm approach. In New trends in agent-based complex automated negotiations (pp. 81–98). Springer, Berlin, Heidelberg.
Mattei, P.A., & Frellsen, J. (2019). MIWAE: deep generative modelling and imputation of incomplete data sets. In International conference on machine learning (pp 4413-4423). PMLR.
Nazabal, A., Olmos, P.M., Ghahramani, Z., & Valera, I. (2020). Handling incomplete heterogeneous data using vaes. Pattern Recognition, 107, 107501.CrossRef
Nguyen, V.V., Pham, B.T., Vu, B.T., Prakash, I., Jha, S., Shahabi, H., & Tien bui, D. (2019). Hybrid machine learning approaches for landslide susceptibility modeling. Forests, 10(2), 157.CrossRef
Opasjumruskit, K., Expósito, J., König-Ries, B., Nauerz, A., & Welsch, M. (2014). Service discovery with personal awareness in smart environments. In Creating personal, social, and urban awareness through pervasive computing (pp 86-107). IGI Global.
Ouksel, A.M., Babad, Y.M., & Tesch, T. (2004). Matchmaking software agents in b2b markets. In 37th Annual hawaii international conference on system sciences, 2004. Proceedings of the (pp. 9–pp). IEEE.
Peng, Y., Junju, L., & Jian, W. (2019). A web service matchmaking approach based on topic models. In 2019 IEEE 9th international conference on electronics information and emergency communication (ICEIEC) (pp. 604-607). IEEE.
Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., & Gloaguen, R. (2020). COVID-19 pandemic prediction for hungary: a hybrid machine learning approach. Mathematics, 8(6), 890.CrossRef
Raman, R., Livny, M., & Solomon, M. (1998). Matchmaking: Distributed resource management for high throughput computing. In Proceedings of the seventh international symposium on high performance distributed computing (Cat. No. 98TB100244) (pp. 140-146). IEEE.
Royston, P., & White, I.R. (2011). Multiple imputation by chained equations (MICE): implementation in Stata. Journal of Statistical Software, 45(4), 1–20.CrossRef
Torabi, M., Mosavi, A., Ozturk, P., Varkonyi-Koczy, A., & Istvan, V. (2018). A hybrid machine learning approach for daily prediction of solar radiation. In International conference on global research and education (pp. 266-274). Springer, Cham.
Tripathy, A., Anand, A., & Rath, S.K. (2017). Document-level sentiment classification using hybrid machine learning approach. Knowledge and Information Systems, 53(3), 805–831.CrossRef
Tripsas, M. (2009). Technology, identity, and inertia through the lens of the digital photography company. Organization Science, 20(2), 441–460.CrossRef
Van Buuren, S., Boshuizen, H.C., & Knook, D.L. (1999). Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine, 18(6), 681–694.CrossRef
Wu, C., Yuan, F., Lu, Y., & Qi, S. (2020). PSM2: A privacy-preserving self-sovereign match-making platform. In International conference on Blockchain and trustworthy systems (pp. 126-141). Springer, Singapore.
Xu, P., Huang, M., Wang, L., & Du, W. (2012). Semantic matchmaking for an agricultural materials exchange platform: an ontology-based approach. In 2012 Second international conference on intelligent system design and engineering application (pp. 913-916). IEEE.
Yoon, J., Jordon, J., & Schaar, M. (2018). Gain: missing data imputation using generative adversarial nets. In International conference on machine learning (pp. 5689-5698). PMLR.
Zapater, J.J.S., Escrivá, D.M.L., García, F.R.S., & Durá, J.J.M. (2015). Semantic web service discovery system for road traffic information services. Expert Systems with Applications, 42(8), 3833–3842.CrossRef
Zhou, S. (1992). Lsf: load sharing in large heterogeneous distributed systems. In I workshop on cluster computing, vol. 136.
de Reuver, M., Sorensen, C., & Basole, R.C. (2018). The digital platform: a research agenda. Journal of Information Technology, 33(2), 124–135.CrossRef
Metadaten
Titel
Supervised Machine Learning for Matchmaking in Digital Business Ecosystems and Platforms
verfasst von
Mustapha Kamal Benramdane
Elena Kornyshova
Samia Bouzefrane
Hubert Maupas
Publikationsdatum
11.01.2023
Verlag
Springer US
Erschienen in
Information Systems Frontiers / Ausgabe 4/2024
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-022-10357-3

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